Intelligent list reading

ABSTRACT

Systems and processes for operating an intelligent automated assistant to perform intelligent list reading are provided. In one example process, a spoken user request associated with a plurality of data items is received. The process determines whether a degree of specificity of the spoken user request is less than a threshold level. In response to determining that a degree of specificity of the spoken user request is less than a threshold level, one or more attributes related to the spoken user request are determined. The one or more attributes are not defined in the spoken user request. Additionally, a list of data items based on the spoken user request and the one or more attributes is obtained. A spoken response comprising a subset of the list of data items is generated and the spoken response is provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/272,214, filed on Sep. 21, 2016, entitled “INTELLIGENT LIST READING,”which claims priority to U.S. Provisional Ser. No. 62/346,236, filed onJun. 6, 2016, entitled “INTELLIGENT LIST READING,” which are herebyincorporated by reference in their entirety for all purposes.

FIELD

This relates generally to intelligent automated assistants and, morespecifically, to intelligent list reading by intelligent automatedassistants.

BACKGROUND

Intelligent automated assistants (or digital assistants) can provide abeneficial interface between human users and electronic devices. Suchassistants can allow users to interact with devices or systems usingnatural language in spoken and/or text forms. For example, a user canprovide a speech input containing a user request to a digital assistantoperating on an electronic device. The digital assistant can interpretthe user's intent from the speech input and operationalize the user'sintent into tasks. The tasks can then be performed by executing one ormore services of the electronic device, and a relevant output responsiveto the user request can be returned to the user.

Interactions with digital assistants can often be voice-based, where theuser provides a spoken user request and the digital assistant replieswith a spoken response that satisfies the request. However, providingintuitive and natural-sounding voice-based interactions with a digitalassistant can be challenging. For example, spoken responses can oftencontain too little or too much information. Additionally, spokenresponses can have awkward transitions and can force the user into oneor more unproductive follow-up interactions. This can impact userexperience and hinder the widespread adoption of digital assistants.

SUMMARY

Systems and processes for operating an intelligent automated assistantto perform intelligent list reading are provided. In one exampleprocess, a spoken user request associated with a plurality of data itemsis received. The process determines whether a degree of specificity ofthe spoken user request is less than a threshold level. In response todetermining that the degree of specificity of the spoken user request isless than a threshold level, one or more attributes related to thespoken user request are determined. The one or more attributes are notdefined in the spoken user request. Additionally, a list of data itemsbased on the spoken user request and the one or more attributes isobtained. A spoken response comprising a subset of the list of dataitems is generated and the spoken response is provided.

In some examples, in response to determining that the degree ofspecificity of the spoken user request is not less than a thresholdlevel, a second list of data items is obtained based on the spoken userrequest. The process further determines whether the number of data itemsin the fourth list of data items exceeds a predetermined number. Inresponse to determining that the number of data items in the fourth listof data items exceeds a predetermined number, a second spoken responsecomprising a subset of the second list of data items is generated andthe second spoken response is provided.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system and environment forimplementing a digital assistant, according to various examples.

FIG. 2A is a block diagram illustrating a portable multifunction deviceimplementing the client-side portion of a digital assistant, accordingto various examples.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling, according to various examples.

FIG. 3 illustrates a portable multifunction device implementing theclient-side portion of a digital assistant, according to variousexamples.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface, according to various examples.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on a portable multifunction device, according to variousexamples.

FIG. 5B illustrates an exemplary user interface for a multifunctiondevice with a touch-sensitive surface that is separate from the display,according to various examples.

FIG. 6A illustrates a personal electronic device, according to variousexamples.

FIG. 6B is a block diagram illustrating a personal electronic device,according to various examples.

FIG. 7A is a block diagram illustrating a digital assistant system or aserver portion thereof, according to various examples.

FIG. 7B illustrates the functions of the digital assistant shown in FIG.7A, according to various examples.

FIG. 7C illustrates a portion of an ontology, according to variousexamples.

FIGS. 8A-D illustrate a process for operating a digital assistant toperform intelligent list reading, according to various examples.

FIGS. 9A-D illustrate intelligent list reading performed by a digitalassistant implemented on a user device in response to spoken userrequests from a user, according to various examples.

FIG. 10 illustrates a functional block diagram of an electronic device,according to various examples.

DETAILED DESCRIPTION

In the following description of examples, reference is made to theaccompanying drawings in which are shown by way of illustration specificexamples that can be practiced. It is to be understood that otherexamples can be used and structural changes can be made withoutdeparting from the scope of the various examples.

As discussed above, providing natural-sounding voice-based interactionswith a digital assistant is challenging. In particular, user requestsrange from being overly broad (e.g., “Any good places to eat?”) to veryspecific (e.g., “What is the meaning of the word ‘plot?’”). If all userrequests were addressed in the same manner, spoken responses provided bythe digital assistant can be awkward and unproductive. For example, inresponse to the user request “Any good places to eat?” the digitalassistant could provide a verbose spoken response containing a long listof restaurants located near the user's current location. Such a responseare overwhelming and unhelpful.

In accordance with some exemplary systems and processes describedherein, the spoken response generated by the digital assistant isintelligently constructed based on the degree of specificity of thespoken user request. In one example process, a spoken user requestassociated with a plurality of data items is received. The processdetermines whether the degree of specificity of the spoken user requestis less than a threshold level. In response to determining that thedegree of specificity of the spoken user request is less than athreshold level, one or more attributes related to the spoken userrequest are determined. The one or more attributes are not defined inthe spoken user request. In particular, the one or more attributes serveto refine the spoken user request, which may be vague and overly broad.A list of data items based on the spoken user request and the one ormore attributes is obtained. A spoken response comprising a subset ofthe list of data items is generated and the spoken response is provided.The subset provides a useful and targeted recommendation to the userinstead of overwhelming the user with a long list of data items.

In some examples, in response to determining that the degree ofspecificity of the spoken user request is not less than a thresholdlevel, a second list of data items is obtained based on the spoken userrequest. Because the degree of specificity is not less than thethreshold level, the second list of data items contain a limited numberof data items. The process determines whether the number of data itemsin the second list of data items exceeds a predetermined number. Thepredetermined number is determined based on various factors, such as theinformation density of each data item in the second list of data items,the user's familiarity with each data item in the second list of dataitems, and a cognitive load associated with the user. In response todetermining that the number of data items in the second list of dataitems exceeds a predetermined number, a second spoken responsecomprising a subset of the second list of data items is generated andthe second spoken response is provided. In this way, the second list ofdata items is presented to the user in smaller groups that is moreeasily processed and retained by the user.

Although the following description uses terms “first,” “second,” etc. todescribe various elements, these elements should not be limited by theterms. These terms are only used to distinguish one element fromanother. For example, a first input could be termed a second input, and,similarly, a second input could be termed a first input, withoutdeparting from the scope of the various described examples. The firstinput and the second input are both inputs and, in some cases, areseparate and different inputs.

The terminology used in the description of the various describedexamples herein is for the purpose of describing particular examplesonly and is not intended to be limiting. As used in the description ofthe various described examples and the appended claims, the singularforms “a,” “an,” and “the” are intended to include the plural forms aswell, unless the context clearly indicates otherwise. It will also beunderstood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes,” “including,” “comprises,” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “upon detecting [the stated condition orevent]” or “in response to detecting [the stated condition or event],”depending on the context.

1. System and Environment

FIG. 1 illustrates a block diagram of system 100 according to variousexamples. In some examples, system 100 implements a digital assistant.The terms “digital assistant,” “virtual assistant,” “intelligentautomated assistant,” or “automatic digital assistant” refer to anyinformation processing system that interprets natural language input inspoken and/or textual form to infer user intent, and performs actionsbased on the inferred user intent. For example, to act on an inferreduser intent, the system performs one or more of the following:identifying a task flow with steps and parameters designed to accomplishthe inferred user intent, inputting specific requirements from theinferred user intent into the task flow; executing the task flow byinvoking programs, methods, services, APIs, or the like; and generatingoutput responses to the user in an audible (e.g., speech) and/or visualform.

Specifically, a digital assistant is capable of accepting a user requestat least partially in the form of a natural language command, request,statement, narrative, and/or inquiry. Typically, the user request seekseither an informational answer or performance of a task by the digitalassistant. A satisfactory response to the user request includes aprovision of the requested informational answer, a performance of therequested task, or a combination of the two. For example, a user asksthe digital assistant a question, such as “Where am I right now?” Basedon the user's current location, the digital assistant answers, “You arein Central Park near the west gate.” The user also requests theperformance of a task, for example, “Please invite my friends to mygirlfriend's birthday party next week.” In response, the digitalassistant can acknowledge the request by saying “Yes, right away,” andthen send a suitable calendar invite on behalf of the user to each ofthe user's friends listed in the user's electronic address book. Duringperformance of a requested task, the digital assistant sometimesinteracts with the user in a continuous dialogue involving multipleexchanges of information over an extended period of time. There arenumerous other ways of interacting with a digital assistant to requestinformation or performance of various tasks. In addition to providingverbal responses and taking programmed actions, the digital assistantalso provides responses in other visual or audio forms, e.g., as text,alerts, music, videos, animations, etc.

As shown in FIG. 1, in some examples, a digital assistant is implementedaccording to a client-server model. The digital assistant includesclient-side portion 102 (hereafter “DA client 102”) executed on userdevice 104 and server-side portion 106 (hereafter “DA server 106”)executed on server system 108. DA client 102 communicates with DA server106 through one or more networks 110. DA client 102 provides client-sidefunctionalities such as user-facing input and output processing andcommunication with DA server 106. DA server 106 provides server-sidefunctionalities for any number of DA clients 102 each residing on arespective user device 104.

In some examples, DA server 106 includes client-facing I/O interface112, one or more processing modules 114, data and models 116, and I/Ointerface to external services 118. The client-facing I/O interface 112facilitates the client-facing input and output processing for DA server106. One or more processing modules 114 utilize data and models 116 toprocess speech input and determine the user's intent based on naturallanguage input. Further, one or more processing modules 114 perform taskexecution based on inferred user intent. In some examples, DA server 106communicates with external services 120 through network(s) 110 for taskcompletion or information acquisition. I/O interface to externalservices 118 facilitates such communications.

User device 104 can be any suitable electronic device. In some examples,user device is a portable multifunctional device (e.g., device 200,described below with reference to FIG. 2A), a multifunctional device(e.g., device 400, described below with reference to FIG. 4), or apersonal electronic device (e.g., device 600, described below withreference to FIG. 6A-B.) A portable multifunctional device is, forexample, a mobile telephone that also contains other functions, such asPDA and/or music player functions. Specific examples of portablemultifunction devices include the iPhone®, iPod Touch®, and iPad®devices from Apple Inc. of Cupertino, Calif. Other examples of portablemultifunction devices include, without limitation, laptop or tabletcomputers. Further, in some examples, user device 104 is a non-portablemultifunctional device. In particular, user device 104 is a desktopcomputer, a game console, a television, or a television set-top box. Insome examples, user device 104 includes a touch-sensitive surface (e.g.,touch screen displays and/or touchpads). Further, user device 104optionally includes one or more other physical user-interface devices,such as a physical keyboard, a mouse, and/or a joystick. Variousexamples of electronic devices, such as multifunctional devices, aredescribed below in greater detail.

Examples of communication network(s) 110 include local area networks(LAN) and wide area networks (WAN), e.g., the Internet. Communicationnetwork(s) 110 is implemented using any known network protocol,including various wired or wireless protocols, such as, for example,Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), codedivision multiple access (CDMA), time division multiple access (TDMA),Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or anyother suitable communication protocol.

Server system 108 is implemented on one or more standalone dataprocessing apparatus or a distributed network of computers. In someexamples, server system 108 also employs various virtual devices and/orservices of third-party service providers (e.g., third-party cloudservice providers) to provide the underlying computing resources and/orinfrastructure resources of server system 108.

In some examples, user device 104 communicates with DA server 106 viasecond user device 122. Second user device 122 is similar or identicalto user device 104. For example, second user device 122 is similar todevices 200, 400, or 600 described below with reference to FIGS. 2A, 4,and 6A-B. User device 104 is configured to communicatively couple tosecond user device 122 via a direct communication connection, such asBluetooth, NFC, BTLE, or the like, or via a wired or wireless network,such as a local Wi-Fi network. In some examples, second user device 122is configured to act as a proxy between user device 104 and DA server106. For example, DA client 102 of user device 104 is configured totransmit information (e.g., a user request received at user device 104)to DA server 106 via second user device 122. DA server 106 processes theinformation and return relevant data (e.g., data content responsive tothe user request) to user device 104 via second user device 122.

In some examples, user device 104 is configured to communicateabbreviated requests for data to second user device 122 to reduce theamount of information transmitted from user device 104. Second userdevice 122 is configured to determine supplemental information to add tothe abbreviated request to generate a complete request to transmit to DAserver 106. This system architecture can advantageously allow userdevice 104 having limited communication capabilities and/or limitedbattery power (e.g., a watch or a similar compact electronic device) toaccess services provided by DA server 106 by using second user device122, having greater communication capabilities and/or battery power(e.g., a mobile phone, laptop computer, tablet computer, or the like),as a proxy to DA server 106. While only two user devices 104 and 122 areshown in FIG. 1, it should be appreciated that system 100, in someexamples, includes any number and type of user devices configured inthis proxy configuration to communicate with DA server system 106.

Although the digital assistant shown in FIG. 1 includes both aclient-side portion (e.g., DA client 102) and a server-side portion(e.g., DA server 106), in some examples, the functions of a digitalassistant are implemented as a standalone application installed on auser device. In addition, the divisions of functionalities between theclient and server portions of the digital assistant can vary indifferent implementations. For instance, in some examples, the DA clientis a thin-client that provides only user-facing input and outputprocessing functions, and delegates all other functionalities of thedigital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices forimplementing the client-side portion of a digital assistant. FIG. 2A isa block diagram illustrating portable multifunction device 200 withtouch-sensitive display system 212 in accordance with some embodiments.Touch-sensitive display 212 is sometimes called a “touch screen” forconvenience and is sometimes known as or called a “touch-sensitivedisplay system.” Device 200 includes memory 202 (which optionallyincludes one or more computer-readable storage mediums), memorycontroller 222, one or more processing units (CPUs) 220, peripheralsinterface 218, RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, input/output (I/O) subsystem 206, other input controldevices 216, and external port 224. Device 200 optionally includes oneor more optical sensors 264. Device 200 optionally includes one or morecontact intensity sensors 265 for detecting intensity of contacts ondevice 200 (e.g., a touch-sensitive surface such as touch-sensitivedisplay system 212 of device 200). Device 200 optionally includes one ormore tactile output generators 267 for generating tactile outputs ondevice 200 (e.g., generating tactile outputs on a touch-sensitivesurface such as touch-sensitive display system 212 of device 200 ortouchpad 455 of device 400). These components optionally communicateover one or more communication buses or signal lines 203.

As used in the specification and claims, the term “intensity” of acontact on a touch-sensitive surface refers to the force or pressure(force per unit area) of a contact (e.g., a finger contact) on thetouch-sensitive surface, or to a substitute (proxy) for the force orpressure of a contact on the touch-sensitive surface. The intensity of acontact has a range of values that includes at least four distinctvalues and more typically includes hundreds of distinct values (e.g., atleast 256). Intensity of a contact is, optionally, determined (ormeasured) using various approaches and various sensors or combinationsof sensors. For example, one or more force sensors underneath oradjacent to the touch-sensitive surface are, optionally, used to measureforce at various points on the touch-sensitive surface. In someimplementations, force measurements from multiple force sensors arecombined (e.g., a weighted average) to determine an estimated force of acontact. Similarly, a pressure-sensitive tip of a stylus is, optionally,used to determine a pressure of the stylus on the touch-sensitivesurface. Alternatively, the size of the contact area detected on thetouch-sensitive surface and/or changes thereto, the capacitance of thetouch-sensitive surface proximate to the contact and/or changes thereto,and/or the resistance of the touch-sensitive surface proximate to thecontact and/or changes thereto are, optionally, used as a substitute forthe force or pressure of the contact on the touch-sensitive surface. Insome implementations, the substitute measurements for contact force orpressure are used directly to determine whether an intensity thresholdhas been exceeded (e.g., the intensity threshold is described in unitscorresponding to the substitute measurements). In some implementations,the substitute measurements for contact force or pressure are convertedto an estimated force or pressure, and the estimated force or pressureis used to determine whether an intensity threshold has been exceeded(e.g., the intensity threshold is a pressure threshold measured in unitsof pressure). Using the intensity of a contact as an attribute of a userinput allows for user access to additional device functionality that mayotherwise not be accessible by the user on a reduced-size device withlimited real estate for displaying affordances (e.g., on atouch-sensitive display) and/or receiving user input (e.g., via atouch-sensitive display, a touch-sensitive surface, or aphysical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output”refers to physical displacement of a device relative to a previousposition of the device, physical displacement of a component (e.g., atouch-sensitive surface) of a device relative to another component(e.g., housing) of the device, or displacement of the component relativeto a center of mass of the device that will be detected by a user withthe user's sense of touch. For example, in situations where the deviceor the component of the device is in contact with a surface of a userthat is sensitive to touch (e.g., a finger, palm, or other part of auser's hand), the tactile output generated by the physical displacementwill be interpreted by the user as a tactile sensation corresponding toa perceived change in physical characteristics of the device or thecomponent of the device. For example, movement of a touch-sensitivesurface (e.g., a touch-sensitive display or trackpad) is, optionally,interpreted by the user as a “down click” or “up click” of a physicalactuator button. In some cases, a user will feel a tactile sensationsuch as an “down click” or “up click” even when there is no movement ofa physical actuator button associated with the touch-sensitive surfacethat is physically pressed (e.g., displaced) by the user's movements. Asanother example, movement of the touch-sensitive surface is, optionally,interpreted or sensed by the user as “roughness” of the touch-sensitivesurface, even when there is no change in smoothness of thetouch-sensitive surface. While such interpretations of touch by a userwill be subject to the individualized sensory perceptions of the user,there are many sensory perceptions of touch that are common to a largemajority of users. Thus, when a tactile output is described ascorresponding to a particular sensory perception of a user (e.g., an “upclick,” a “down click,” “roughness”), unless otherwise stated, thegenerated tactile output corresponds to physical displacement of thedevice or a component thereof that will generate the described sensoryperception for a typical (or average) user.

It should be appreciated that device 200 is only one example of aportable multifunction device, and that device 200 optionally has moreor fewer components than shown, optionally combines two or morecomponents, or optionally has a different configuration or arrangementof the components. The various components shown in FIG. 2A areimplemented in hardware, software, or a combination of both hardware andsoftware, including one or more signal processing and/orapplication-specific integrated circuits.

Memory 202 includes one or more computer-readable storage mediums. Thecomputer-readable storage mediums are, for example, tangible andnon-transitory. Memory 202 includes high-speed random access memory andalso includes non-volatile memory, such as one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices. Memory controller 222 controls access to memory 202 byother components of device 200.

In some examples, a non-transitory computer-readable storage medium ofmemory 202 is used to store instructions (e.g., for performing aspectsof processes described below) for use by or in connection with aninstruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device and execute the instructions. In other examples,the instructions (e.g., for performing aspects of the processesdescribed below) are stored on a non-transitory computer-readablestorage medium (not shown) of the server system 108 or are dividedbetween the non-transitory computer-readable storage medium of memory202 and the non-transitory computer-readable storage medium of serversystem 108.

Peripherals interface 218 is used to couple input and output peripheralsof the device to CPU 220 and memory 202. The one or more processors 220run or execute various software programs and/or sets of instructionsstored in memory 202 to perform various functions for device 200 and toprocess data. In some embodiments, peripherals interface 218, CPU 220,and memory controller 222 are implemented on a single chip, such as chip204. In some other embodiments, they are implemented on separate chips.

RF (radio frequency) circuitry 208 receives and sends RF signals, alsocalled electromagnetic signals. RF circuitry 208 converts electricalsignals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. RF circuitry 208 optionally includes well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 208 optionally communicates with networks, such as theInternet, also referred to as the World Wide Web (WWW), an intranetand/or a wireless network, such as a cellular telephone network, awireless local area network (LAN) and/or a metropolitan area network(MAN), and other devices by wireless communication. The RF circuitry 208optionally includes well-known circuitry for detecting near fieldcommunication (NFC) fields, such as by a short-range communicationradio. The wireless communication optionally uses any of a plurality ofcommunications standards, protocols, and technologies, including but notlimited to Global System for Mobile Communications (GSM), Enhanced DataGSM Environment (EDGE), high-speed downlink packet access (HSDPA),high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO),HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), nearfield communication (NFC), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity(Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n,and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, aprotocol for e mail (e.g., Internet message access protocol (IMAP)and/or post office protocol (POP)), instant messaging (e.g., extensiblemessaging and presence protocol (XMPP), Session Initiation Protocol forInstant Messaging and Presence Leveraging Extensions (SIMPLE), InstantMessaging and Presence Service (IMPS)), and/or Short Message Service(SMS), or any other suitable communication protocol, includingcommunication protocols not yet developed as of the filing date of thisdocument.

Audio circuitry 210, speaker 211, and microphone 213 provide an audiointerface between a user and device 200. Audio circuitry 210 receivesaudio data from peripherals interface 218, converts the audio data to anelectrical signal, and transmits the electrical signal to speaker 211.Speaker 211 converts the electrical signal to human-audible sound waves.Audio circuitry 210 also receives electrical signals converted bymicrophone 213 from sound waves. Audio circuitry 210 converts theelectrical signal to audio data and transmits the audio data toperipherals interface 218 for processing. Audio data are retrieved fromand/or transmitted to memory 202 and/or RF circuitry 208 by peripheralsinterface 218. In some embodiments, audio circuitry 210 also includes aheadset jack (e.g., 312, FIG. 3). The headset jack provides an interfacebetween audio circuitry 210 and removable audio input/outputperipherals, such as output-only headphones or a headset with bothoutput (e.g., a headphone for one or both ears) and input (e.g., amicrophone).

I/O subsystem 206 couples input/output peripherals on device 200, suchas touch screen 212 and other input control devices 216, to peripheralsinterface 218. I/O subsystem 206 optionally includes display controller256, optical sensor controller 258, intensity sensor controller 259,haptic feedback controller 261, and one or more input controllers 260for other input or control devices. The one or more input controllers260 receive/send electrical signals from/to other input control devices216. The other input control devices 216 optionally include physicalbuttons (e.g., push buttons, rocker buttons, etc.), dials, sliderswitches, joysticks, click wheels, and so forth. In some alternateembodiments, input controller(s) 260 are, optionally, coupled to any (ornone) of the following: a keyboard, an infrared port, a USB port, and apointer device such as a mouse. The one or more buttons (e.g., 308, FIG.3) optionally include an up/down button for volume control of speaker211 and/or microphone 213. The one or more buttons optionally include apush button (e.g., 306, FIG. 3).

A quick press of the push button disengages a lock of touch screen 212or begin a process that uses gestures on the touch screen to unlock thedevice, as described in U.S. patent application Ser. No. 11/322,549,“Unlocking a Device by Performing Gestures on an Unlock Image,” filedDec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated byreference in its entirety. A longer press of the push button (e.g., 306)turns power to device 200 on or off. The user is able to customize afunctionality of one or more of the buttons. Touch screen 212 is used toimplement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 212 provides an input interface and an outputinterface between the device and a user. Display controller 256 receivesand/or sends electrical signals from/to touch screen 212. Touch screen212 displays visual output to the user. The visual output includesgraphics, text, icons, video, and any combination thereof (collectivelytermed “graphics”). In some embodiments, some or all of the visualoutput correspond to user-interface objects.

Touch screen 212 has a touch-sensitive surface, sensor, or set ofsensors that accepts input from the user based on haptic and/or tactilecontact. Touch screen 212 and display controller 256 (along with anyassociated modules and/or sets of instructions in memory 202) detectcontact (and any movement or breaking of the contact) on touch screen212 and convert the detected contact into interaction withuser-interface objects (e.g., one or more soft keys, icons, web pages,or images) that are displayed on touch screen 212. In an exemplaryembodiment, a point of contact between touch screen 212 and the usercorresponds to a finger of the user.

Touch screen 212 uses LCD (liquid crystal display) technology, LPD(light emitting polymer display) technology, or LED (light emittingdiode) technology, although other display technologies may be used inother embodiments. Touch screen 212 and display controller 256 detectcontact and any movement or breaking thereof using any of a plurality oftouch sensing technologies now known or later developed, including butnot limited to capacitive, resistive, infrared, and surface acousticwave technologies, as well as other proximity sensor arrays or otherelements for determining one or more points of contact with touch screen212. In an exemplary embodiment, projected mutual capacitance sensingtechnology is used, such as that found in the iPhone® and iPod Touch®from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 212 isanalogous to the multi-touch sensitive touchpads described in thefollowing U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No.6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932(Westerman), and/or U.S. Patent Publication 2002/0015024A1, each ofwhich is hereby incorporated by reference in its entirety. However,touch screen 212 displays visual output from device 200, whereastouch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 212 is asdescribed in the following applications: (1) U.S. patent applicationSer. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2,2006; (2) U.S. patent application Ser. No. 10/840,862, “MultipointTouchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No.10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30,2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures ForTouch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patentapplication Ser. No. 11/038,590, “Mode-Based Graphical User InterfacesFor Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patentapplication Ser. No. 11/228,758, “Virtual Input Device Placement On ATouch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patentapplication Ser. No. 11/228,700, “Operation Of A Computer With A TouchScreen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser.No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen VirtualKeyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No.11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. Allof these applications are incorporated by reference herein in theirentirety.

Touch screen 212 has, for example, a video resolution in excess of 100dpi. In some embodiments, the touch screen has a video resolution ofapproximately 160 dpi. The user makes contact with touch screen 212using any suitable object or appendage, such as a stylus, a finger, andso forth. In some embodiments, the user interface is designed to workprimarily with finger-based contacts and gestures, which can be lessprecise than stylus-based input due to the larger area of contact of afinger on the touch screen. In some embodiments, the device translatesthe rough finger-based input into a precise pointer/cursor position orcommand for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 200includes a touchpad (not shown) for activating or deactivatingparticular functions. In some embodiments, the touchpad is atouch-sensitive area of the device that, unlike the touch screen, doesnot display visual output. The touchpad is a touch-sensitive surfacethat is separate from touch screen 212 or an extension of thetouch-sensitive surface formed by the touch screen.

Device 200 also includes power system 262 for powering the variouscomponents. Power system 262 includes a power management system, one ormore power sources (e.g., battery, alternating current (AC)), arecharging system, a power failure detection circuit, a power converteror inverter, a power status indicator (e.g., a light-emitting diode(LED)) and any other components associated with the generation,management and distribution of power in portable devices.

Device 200 also includes one or more optical sensors 264. FIG. 2A showsan optical sensor coupled to optical sensor controller 258 in I/Osubsystem 206. Optical sensor 264 includes charge-coupled device (CCD)or complementary metal-oxide semiconductor (CMOS) phototransistors.Optical sensor 264 receives light from the environment, projectedthrough one or more lenses, and converts the light to data representingan image. In conjunction with imaging module 243 (also called a cameramodule), optical sensor 264 captures still images or video. In someembodiments, an optical sensor is located on the back of device 200,opposite touch screen display 212 on the front of the device so that thetouch screen display is used as a viewfinder for still and/or videoimage acquisition. In some embodiments, an optical sensor is located onthe front of the device so that the user's image is obtained for videoconferencing while the user views the other video conferenceparticipants on the touch screen display. In some embodiments, theposition of optical sensor 264 can be changed by the user (e.g., byrotating the lens and the sensor in the device housing) so that a singleoptical sensor 264 is used along with the touch screen display for bothvideo conferencing and still and/or video image acquisition.

Device 200 optionally also includes one or more contact intensitysensors 265. FIG. 2A shows a contact intensity sensor coupled tointensity sensor controller 259 in I/O subsystem 206. Contact intensitysensor 265 optionally includes one or more piezoresistive strain gauges,capacitive force sensors, electric force sensors, piezoelectric forcesensors, optical force sensors, capacitive touch-sensitive surfaces, orother intensity sensors (e.g., sensors used to measure the force (orpressure) of a contact on a touch-sensitive surface). Contact intensitysensor 265 receives contact intensity information (e.g., pressureinformation or a proxy for pressure information) from the environment.In some embodiments, at least one contact intensity sensor is collocatedwith, or proximate to, a touch-sensitive surface (e.g., touch-sensitivedisplay system 212). In some embodiments, at least one contact intensitysensor is located on the back of device 200, opposite touch screendisplay 212, which is located on the front of device 200.

Device 200 also includes one or more proximity sensors 266. FIG. 2Ashows proximity sensor 266 coupled to peripherals interface 218.Alternately, proximity sensor 266 is coupled to input controller 260 inI/O subsystem 206. Proximity sensor 266 is performed as described inU.S. patent application Ser. No. 11/241,839, “Proximity Detector InHandheld Device”; Ser. No. 11/240,788, “Proximity Detector In HandheldDevice”; Ser. No. 11/620,702, “Using Ambient Light Sensor To AugmentProximity Sensor Output”; Ser. No. 11/586,862, “Automated Response ToAnd Sensing Of User Activity In Portable Devices”; and Ser. No.11/638,251, “Methods And Systems For Automatic Configuration OfPeripherals,” which are hereby incorporated by reference in theirentirety. In some embodiments, the proximity sensor turns off anddisables touch screen 212 when the multifunction device is placed nearthe user's ear (e.g., when the user is making a phone call).

Device 200 optionally also includes one or more tactile outputgenerators 267. FIG. 2A shows a tactile output generator coupled tohaptic feedback controller 261 in I/O subsystem 206. Tactile outputgenerator 267 optionally includes one or more electroacoustic devicessuch as speakers or other audio components and/or electromechanicaldevices that convert energy into linear motion such as a motor,solenoid, electroactive polymer, piezoelectric actuator, electrostaticactuator, or other tactile output generating component (e.g., acomponent that converts electrical signals into tactile outputs on thedevice). Contact intensity sensor 265 receives tactile feedbackgeneration instructions from haptic feedback module 233 and generatestactile outputs on device 200 that are capable of being sensed by a userof device 200. In some embodiments, at least one tactile outputgenerator is collocated with, or proximate to, a touch-sensitive surface(e.g., touch-sensitive display system 212) and, optionally, generates atactile output by moving the touch-sensitive surface vertically (e.g.,in/out of a surface of device 200) or laterally (e.g., back and forth inthe same plane as a surface of device 200). In some embodiments, atleast one tactile output generator sensor is located on the back ofdevice 200, opposite touch screen display 212, which is located on thefront of device 200.

Device 200 also includes one or more accelerometers 268. FIG. 2A showsaccelerometer 268 coupled to peripherals interface 218. Alternately,accelerometer 268 is coupled to an input controller 260 in I/O subsystem206. Accelerometer 268 performs, for example, as described in U.S.Patent Publication No. 20050190059, “Acceleration-based Theft DetectionSystem for Portable Electronic Devices,” and U.S. Patent Publication No.20060017692, “Methods And Apparatuses For Operating A Portable DeviceBased On An Accelerometer,” both of which are incorporated by referenceherein in their entirety. In some embodiments, information is displayedon the touch screen display in a portrait view or a landscape view basedon an analysis of data received from the one or more accelerometers.Device 200 optionally includes, in addition to accelerometer(s) 268, amagnetometer (not shown) and a GPS (or GLONASS or other globalnavigation system) receiver (not shown) for obtaining informationconcerning the location and orientation (e.g., portrait or landscape) ofdevice 200.

In some embodiments, the software components stored in memory 202include operating system 226, communication module (or set ofinstructions) 228, contact/motion module (or set of instructions) 230,graphics module (or set of instructions) 232, text input module (or setof instructions) 234, Global Positioning System (GPS) module (or set ofinstructions) 235, Digital Assistant Client Module 229, and applications(or sets of instructions) 236. Further, memory 202 stores data andmodels, such as user data and models 231. Furthermore, in someembodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/globalinternal state 257, as shown in FIGS. 2A and 4. Device/global internalstate 257 includes one or more of: active application state, indicatingwhich applications, if any, are currently active; display state,indicating what applications, views or other information occupy variousregions of touch screen display 212; sensor state, including informationobtained from the device's various sensors and input control devices216; and location information concerning the device's location and/orattitude.

Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communication between varioushardware and software components.

Communication module 228 facilitates communication with other devicesover one or more external ports 224 and also includes various softwarecomponents for handling data received by RF circuitry 208 and/orexternal port 224. External port 224 (e.g., Universal Serial Bus (USB),FIREWIRE, etc.) is adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.). Insome embodiments, the external port is a multi-pin (e.g., 30-pin)connector that is the same as, or similar to and/or compatible with, the30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 230 optionally detects contact with touch screen212 (in conjunction with display controller 256) and othertouch-sensitive devices (e.g., a touchpad or physical click wheel).Contact/motion module 230 includes various software components forperforming various operations related to detection of contact, such asdetermining if contact has occurred (e.g., detecting a finger-downevent), determining an intensity of the contact (e.g., the force orpressure of the contact or a substitute for the force or pressure of thecontact), determining if there is movement of the contact and trackingthe movement across the touch-sensitive surface (e.g., detecting one ormore finger-dragging events), and determining if the contact has ceased(e.g., detecting a finger-up event or a break in contact).Contact/motion module 230 receives contact data from the touch-sensitivesurface. Determining movement of the point of contact, which isrepresented by a series of contact data, optionally includes determiningspeed (magnitude), velocity (magnitude and direction), and/or anacceleration (a change in magnitude and/or direction) of the point ofcontact. These operations are, optionally, applied to single contacts(e.g., one finger contacts) or to multiple simultaneous contacts (e.g.,“multitouch”/multiple finger contacts). In some embodiments,contact/motion module 230 and display controller 256 detect contact on atouchpad.

In some embodiments, contact/motion module 230 uses a set of one or moreintensity thresholds to determine whether an operation has beenperformed by a user (e.g., to determine whether a user has “clicked” onan icon). In some embodiments, at least a subset of the intensitythresholds are determined in accordance with software parameters (e.g.,the intensity thresholds are not determined by the activation thresholdsof particular physical actuators and can be adjusted without changingthe physical hardware of device 200). For example, a mouse “click”threshold of a trackpad or touch screen display can be set to any of alarge range of predefined threshold values without changing the trackpador touch screen display hardware. Additionally, in some implementations,a user of the device is provided with software settings for adjustingone or more of the set of intensity thresholds (e.g., by adjustingindividual intensity thresholds and/or by adjusting a plurality ofintensity thresholds at once with a system-level click “intensity”parameter).

Contact/motion module 230 optionally detects a gesture input by a user.Different gestures on the touch-sensitive surface have different contactpatterns (e.g., different motions, timings, and/or intensities ofdetected contacts). Thus, a gesture is, optionally, detected bydetecting a particular contact pattern. For example, detecting a fingertap gesture includes detecting a finger-down event followed by detectinga finger-up (liftoff) event at the same position (or substantially thesame position) as the finger-down event (e.g., at the position of anicon). As another example, detecting a finger swipe gesture on thetouch-sensitive surface includes detecting a finger-down event followedby detecting one or more finger-dragging events, and subsequentlyfollowed by detecting a finger-up (liftoff) event.

Graphics module 232 includes various known software components forrendering and displaying graphics on touch screen 212 or other display,including components for changing the visual impact (e.g., brightness,transparency, saturation, contrast, or other visual property) ofgraphics that are displayed. As used herein, the term “graphics”includes any object that can be displayed to a user, including, withoutlimitation, text, web pages, icons (such as user-interface objectsincluding soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 232 stores data representinggraphics to be used. Each graphic is, optionally, assigned acorresponding code. Graphics module 232 receives, from applicationsetc., one or more codes specifying graphics to be displayed along with,if necessary, coordinate data and other graphic property data, and thengenerates screen image data to output to display controller 256.

Haptic feedback module 233 includes various software components forgenerating instructions used by tactile output generator(s) 267 toproduce tactile outputs at one or more locations on device 200 inresponse to user interactions with device 200.

Text input module 234, which is, in some examples, a component ofgraphics module 232, provides soft keyboards for entering text invarious applications (e.g., contacts 237, email 240, IM 241, browser247, and any other application that needs text input).

GPS module 235 determines the location of the device and provides thisinformation for use in various applications (e.g., to telephone 238 foruse in location-based dialing; to camera 243 as picture/video metadata;and to applications that provide location-based services such as weatherwidgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module 229 includes various client-side digitalassistant instructions to provide the client-side functionalities of thedigital assistant. For example, digital assistant client module 229 iscapable of accepting voice input (e.g., speech input), text input, touchinput, and/or gestural input through various user interfaces (e.g.,microphone 213, accelerometer(s) 268, touch-sensitive display system212, optical sensor(s) 264, other input control devices 216, etc.) ofportable multifunction device 200. Digital assistant client module 229is also capable of providing output in audio (e.g., speech output),visual, and/or tactile forms through various output interfaces (e.g.,speaker 211, touch-sensitive display system 212, tactile outputgenerator(s) 267, etc.) of portable multifunction device 200. Forexample, output is provided as voice, sound, alerts, text messages,menus, graphics, videos, animations, vibrations, and/or combinations oftwo or more of the above. During operation, digital assistant clientmodule 229 communicates with DA server 106 using RF circuitry 208.

User data and models 231 include various data associated with the user(e.g., user-specific vocabulary data, user preference data,user-specified name pronunciations, data from the user's electronicaddress book, to-do lists, shopping lists, etc.) to provide theclient-side functionalities of the digital assistant. Further, user dataand models 231 include various models (e.g., speech recognition models,statistical language models, natural language processing models,ontology, task flow models, service models, etc.) for processing userinput and determining user intent.

In some examples, digital assistant client module 229 utilizes thevarious sensors, subsystems, and peripheral devices of portablemultifunction device 200 to gather additional information from thesurrounding environment of the portable multifunction device 200 toestablish a context associated with a user, the current userinteraction, and/or the current user input. In some examples, digitalassistant client module 229 provides the contextual information or asubset thereof with the user input to DA server 106 to help infer theuser's intent. In some examples, the digital assistant also uses thecontextual information to determine how to prepare and deliver outputsto the user. Contextual information is referred to as context data.

In some examples, the contextual information that accompanies the userinput includes sensor information, e.g., lighting, ambient noise,ambient temperature, images or videos of the surrounding environment,etc. In some examples, the contextual information can also includes thephysical state of the device, e.g., device orientation, device location,device temperature, power level, speed, acceleration, motion patterns,cellular signals strength, etc. In some examples, information related tothe software state of DA server 106, e.g., running processes, installedprograms, past and present network activities, background services,error logs, resources usage, etc., and of portable multifunction device200 is provided to DA server 106 as contextual information associatedwith a user input.

In some examples, the digital assistant client module 229 selectivelyprovides information (e.g., user data 231) stored on the portablemultifunction device 200 in response to requests from DA server 106. Insome examples, digital assistant client module 229 also elicitsadditional input from the user via a natural language dialogue or otheruser interfaces upon request by DA server 106. Digital assistant clientmodule 229 passes the additional input to DA server 106 to help DAserver 106 in intent deduction and/or fulfillment of the user's intentexpressed in the user request.

A more detailed description of a digital assistant is described belowwith reference to FIGS. 7A-C. It should be recognized that digitalassistant client module 229 can include any number of the sub-modules ofdigital assistant module 726 described below.

Applications 236 include the following modules (or sets ofinstructions), or a subset or superset thereof:

-   -   Contacts module 237 (sometimes called an address book or contact        list);    -   Telephone module 238;    -   Video conference module 239;    -   E-mail client module 240;    -   Instant messaging (IM) module 241;    -   Workout support module 242;    -   Camera module 243 for still and/or video images;    -   Image management module 244;    -   Video player module;    -   Music player module;    -   Browser module 247;    -   Calendar module 248;    -   Widget modules 249, which includes, in some examples, one or        more of: weather widget 249-1, stocks widget 249-2, calculator        widget 249-3, alarm clock widget 249-4, dictionary widget 249-5,        and other widgets obtained by the user, as well as user-created        widgets 249-6;    -   Widget creator module 250 for making user-created widgets 249-6;    -   Search module 251;    -   Video and music player module 252, which merges video player        module and music player module;    -   Notes module 253;    -   Map module 254; and/or    -   Online video module 255.

Examples of other applications 236 that are stored in memory 202 includeother word processing applications, other image editing applications,drawing applications, presentation applications, JAVA-enabledapplications, encryption, digital rights management, voice recognition,and voice replication.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, contacts module 237 are used to manage an address book or contactlist (e.g., stored in application internal state 292 of contacts module237 in memory 202 or memory 470), including: adding name(s) to theaddress book; deleting name(s) from the address book; associatingtelephone number(s), e-mail address(es), physical address(es) or otherinformation with a name; associating an image with a name; categorizingand sorting names; providing telephone numbers or e-mail addresses toinitiate and/or facilitate communications by telephone 238, videoconference module 239, e-mail 240, or IM 241; and so forth.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, contact/motionmodule 230, graphics module 232, and text input module 234, telephonemodule 238 are used to enter a sequence of characters corresponding to atelephone number, access one or more telephone numbers in contactsmodule 237, modify a telephone number that has been entered, dial arespective telephone number, conduct a conversation, and disconnect orhang up when the conversation is completed. As noted above, the wirelesscommunication uses any of a plurality of communications standards,protocols, and technologies.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, optical sensor264, optical sensor controller 258, contact/motion module 230, graphicsmodule 232, text input module 234, contacts module 237, and telephonemodule 238, video conference module 239 includes executable instructionsto initiate, conduct, and terminate a video conference between a userand one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, e-mail client module 240 includes executableinstructions to create, send, receive, and manage e-mail in response touser instructions. In conjunction with image management module 244,e-mail client module 240 makes it very easy to create and send e-mailswith still or video images taken with camera module 243.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, the instant messaging module 241 includes executableinstructions to enter a sequence of characters corresponding to aninstant message, to modify previously entered characters, to transmit arespective instant message (for example, using a Short Message Service(SMS) or Multimedia Message Service (MMS) protocol for telephony-basedinstant messages or using XMPP, SIMPLE, or IMPS for Internet-basedinstant messages), to receive instant messages, and to view receivedinstant messages. In some embodiments, transmitted and/or receivedinstant messages include graphics, photos, audio files, video filesand/or other attachments as are supported in an MMS and/or an EnhancedMessaging Service (EMS). As used herein, “instant messaging” refers toboth telephony-based messages (e.g., messages sent using SMS or MMS) andInternet-based messages (e.g., messages sent using XMPP, SIMPLE, orIMPS).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, map module 254, and music playermodule, workout support module 242 includes executable instructions tocreate workouts (e.g., with time, distance, and/or calorie burninggoals); communicate with workout sensors (sports devices); receiveworkout sensor data; calibrate sensors used to monitor a workout; selectand play music for a workout; and display, store, and transmit workoutdata.

In conjunction with touch screen 212, display controller 256, opticalsensor(s) 264, optical sensor controller 258, contact/motion module 230,graphics module 232, and image management module 244, camera module 243includes executable instructions to capture still images or video(including a video stream) and store them into memory 202, modifycharacteristics of a still image or video, or delete a still image orvideo from memory 202.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, text input module 234,and camera module 243, image management module 244 includes executableinstructions to arrange, modify (e.g., edit), or otherwise manipulate,label, delete, present (e.g., in a digital slide show or album), andstore still and/or video images.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, browser module 247 includes executable instructions tobrowse the Internet in accordance with user instructions, includingsearching, linking to, receiving, and displaying web pages or portionsthereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, e-mail client module 240, and browser module 247,calendar module 248 includes executable instructions to create, display,modify, and store calendars and data associated with calendars (e.g.,calendar entries, to-do lists, etc.) in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, widget modules 249 aremini-applications that can be downloaded and used by a user (e.g.,weather widget 249-1, stocks widget 249-2, calculator widget 249-3,alarm clock widget 249-4, and dictionary widget 249-5) or created by theuser (e.g., user-created widget 249-6). In some embodiments, a widgetincludes an HTML (Hypertext Markup Language) file, a CSS (CascadingStyle Sheets) file, and a JavaScript file. In some embodiments, a widgetincludes an XML (Extensible Markup Language) file and a JavaScript file(e.g., Yahoo! Widgets).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, the widget creator module 250are used by a user to create widgets (e.g., turning a user-specifiedportion of a web page into a widget).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, search module 251 includes executable instructions to search fortext, music, sound, image, video, and/or other files in memory 202 thatmatch one or more search criteria (e.g., one or more user-specifiedsearch terms) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, and browser module 247, video and musicplayer module 252 includes executable instructions that allow the userto download and play back recorded music and other sound files stored inone or more file formats, such as MP3 or AAC files, and executableinstructions to display, present, or otherwise play back videos (e.g.,on touch screen 212 or on an external, connected display via externalport 224). In some embodiments, device 200 optionally includes thefunctionality of an MP3 player, such as an iPod (trademark of AppleInc.).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, notes module 253 includes executable instructions to create andmanage notes, to-do lists, and the like in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, and browser module 247, map module 254are used to receive, display, modify, and store maps and data associatedwith maps (e.g., driving directions, data on stores and other points ofinterest at or near a particular location, and other location-baseddata) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, text input module 234, e-mail clientmodule 240, and browser module 247, online video module 255 includesinstructions that allow the user to access, browse, receive (e.g., bystreaming and/or download), play back (e.g., on the touch screen or onan external, connected display via external port 224), send an e-mailwith a link to a particular online video, and otherwise manage onlinevideos in one or more file formats, such as H.264. In some embodiments,instant messaging module 241, rather than e-mail client module 240, isused to send a link to a particular online video. Additional descriptionof the online video application can be found in U.S. Provisional PatentApplication No. 60/936,562, “Portable Multifunction Device, Method, andGraphical User Interface for Playing Online Videos,” filed Jun. 20,2007, and U.S. patent application Ser. No. 11/968,067, “PortableMultifunction Device, Method, and Graphical User Interface for PlayingOnline Videos,” filed Dec. 31, 2007, the contents of which are herebyincorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to aset of executable instructions for performing one or more functionsdescribed above and the methods described in this application (e.g., thecomputer-implemented methods and other information processing methodsdescribed herein). These modules (e.g., sets of instructions) need notbe implemented as separate software programs, procedures, or modules,and thus various subsets of these modules can be combined or otherwiserearranged in various embodiments. For example, video player module canbe combined with music player module into a single module (e.g., videoand music player module 252, FIG. 2A). In some embodiments, memory 202stores a subset of the modules and data structures identified above.Furthermore, memory 202 stores additional modules and data structuresnot described above.

In some embodiments, device 200 is a device where operation of apredefined set of functions on the device is performed exclusivelythrough a touch screen and/or a touchpad. By using a touch screen and/ora touchpad as the primary input control device for operation of device200, the number of physical input control devices (such as push buttons,dials, and the like) on device 200 is reduced.

The predefined set of functions that are performed exclusively through atouch screen and/or a touchpad optionally include navigation betweenuser interfaces. In some embodiments, the touchpad, when touched by theuser, navigates device 200 to a main, home, or root menu from any userinterface that is displayed on device 200. In such embodiments, a “menubutton” is implemented using a touchpad. In some other embodiments, themenu button is a physical push button or other physical input controldevice instead of a touchpad.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments. In some embodiments,memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., inoperating system 226) and a respective application 236-1 (e.g., any ofthe aforementioned applications 237-251, 255, 480-490).

Event sorter 270 receives event information and determines theapplication 236-1 and application view 291 of application 236-1 to whichto deliver the event information. Event sorter 270 includes eventmonitor 271 and event dispatcher module 274. In some embodiments,application 236-1 includes application internal state 292, whichindicates the current application view(s) displayed on touch-sensitivedisplay 212 when the application is active or executing. In someembodiments, device/global internal state 257 is used by event sorter270 to determine which application(s) is (are) currently active, andapplication internal state 292 is used by event sorter 270 to determineapplication views 291 to which to deliver event information.

In some embodiments, application internal state 292 includes additionalinformation, such as one or more of: resume information to be used whenapplication 236-1 resumes execution, user interface state informationthat indicates information being displayed or that is ready for displayby application 236-1, a state queue for enabling the user to go back toa prior state or view of application 236-1, and a redo/undo queue ofprevious actions taken by the user.

Event monitor 271 receives event information from peripherals interface218. Event information includes information about a sub-event (e.g., auser touch on touch-sensitive display 212, as part of a multi-touchgesture). Peripherals interface 218 transmits information it receivesfrom I/O subsystem 206 or a sensor, such as proximity sensor 266,accelerometer(s) 268, and/or microphone 213 (through audio circuitry210). Information that peripherals interface 218 receives from I/Osubsystem 206 includes information from touch-sensitive display 212 or atouch-sensitive surface.

In some embodiments, event monitor 271 sends requests to the peripheralsinterface 218 at predetermined intervals. In response, peripheralsinterface 218 transmits event information. In other embodiments,peripherals interface 218 transmits event information only when there isa significant event (e.g., receiving an input above a predeterminednoise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 270 also includes a hit viewdetermination module 272 and/or an active event recognizer determinationmodule 273.

Hit view determination module 272 provides software procedures fordetermining where a sub-event has taken place within one or more viewswhen touch-sensitive display 212 displays more than one view. Views aremade up of controls and other elements that a user can see on thedisplay.

Another aspect of the user interface associated with an application is aset of views, sometimes herein called application views or userinterface windows, in which information is displayed and touch-basedgestures occur. The application views (of a respective application) inwhich a touch is detected correspond to programmatic levels within aprogrammatic or view hierarchy of the application. For example, thelowest level view in which a touch is detected is called the hit view,and the set of events that are recognized as proper inputs is determinedbased, at least in part, on the hit view of the initial touch thatbegins a touch-based gesture.

Hit view determination module 272 receives information related to subevents of a touch-based gesture. When an application has multiple viewsorganized in a hierarchy, hit view determination module 272 identifies ahit view as the lowest view in the hierarchy which should handle thesub-event. In most circumstances, the hit view is the lowest level viewin which an initiating sub-event occurs (e.g., the first sub-event inthe sequence of sub-events that form an event or potential event). Oncethe hit view is identified by the hit view determination module 272, thehit view typically receives all sub-events related to the same touch orinput source for which it was identified as the hit view.

Active event recognizer determination module 273 determines which viewor views within a view hierarchy should receive a particular sequence ofsub-events. In some embodiments, active event recognizer determinationmodule 273 determines that only the hit view should receive a particularsequence of sub-events. In other embodiments, active event recognizerdetermination module 273 determines that all views that include thephysical location of a sub-event are actively involved views, andtherefore determines that all actively involved views should receive aparticular sequence of sub-events. In other embodiments, even if touchsub-events were entirely confined to the area associated with oneparticular view, views higher in the hierarchy would still remain asactively involved views.

Event dispatcher module 274 dispatches the event information to an eventrecognizer (e.g., event recognizer 280). In embodiments including activeevent recognizer determination module 273, event dispatcher module 274delivers the event information to an event recognizer determined byactive event recognizer determination module 273. In some embodiments,event dispatcher module 274 stores in an event queue the eventinformation, which is retrieved by a respective event receiver 282.

In some embodiments, operating system 226 includes event sorter 270.Alternatively, application 236-1 includes event sorter 270. In yet otherembodiments, event sorter 270 is a stand-alone module, or a part ofanother module stored in memory 202, such as contact/motion module 230.

In some embodiments, application 236-1 includes a plurality of eventhandlers 290 and one or more application views 291, each of whichincludes instructions for handling touch events that occur within arespective view of the application's user interface. Each applicationview 291 of the application 236-1 includes one or more event recognizers280. Typically, a respective application view 291 includes a pluralityof event recognizers 280. In other embodiments, one or more of eventrecognizers 280 are part of a separate module, such as a user interfacekit (not shown) or a higher level object from which application 236-1inherits methods and other properties. In some embodiments, a respectiveevent handler 290 includes one or more of: data updater 276, objectupdater 277, GUI updater 278, and/or event data 279 received from eventsorter 270. Event handler 290 utilizes or calls data updater 276, objectupdater 277, or GUI updater 278 to update the application internal state292. Alternatively, one or more of the application views 291 include oneor more respective event handlers 290. Also, in some embodiments, one ormore of data updater 276, object updater 277, and GUI updater 278 areincluded in a respective application view 291.

A respective event recognizer 280 receives event information (e.g.,event data 279) from event sorter 270 and identifies an event from theevent information. Event recognizer 280 includes event receiver 282 andevent comparator 284. In some embodiments, event recognizer 280 alsoincludes at least a subset of: metadata 283, and event deliveryinstructions 288 (which include sub-event delivery instructions).

Event receiver 282 receives event information from event sorter 270. Theevent information includes information about a sub-event, for example, atouch or a touch movement. Depending on the sub-event, the eventinformation also includes additional information, such as location ofthe sub-event. When the sub-event concerns motion of a touch, the eventinformation also includes speed and direction of the sub-event. In someembodiments, events include rotation of the device from one orientationto another (e.g., from a portrait orientation to a landscapeorientation, or vice versa), and the event information includescorresponding information about the current orientation (also calleddevice attitude) of the device.

Event comparator 284 compares the event information to predefined eventor sub-event definitions and, based on the comparison, determines anevent or sub event, or determines or updates the state of an event orsub-event. In some embodiments, event comparator 284 includes eventdefinitions 286. Event definitions 286 contain definitions of events(e.g., predefined sequences of sub-events), for example, event 1(287-1), event 2 (287-2), and others. In some embodiments, sub-events inan event (287) include, for example, touch begin, touch end, touchmovement, touch cancellation, and multiple touching. In one example, thedefinition for event 1 (287-1) is a double tap on a displayed object.The double tap, for example, comprises a first touch (touch begin) onthe displayed object for a predetermined phase, a first liftoff (touchend) for a predetermined phase, a second touch (touch begin) on thedisplayed object for a predetermined phase, and a second liftoff (touchend) for a predetermined phase. In another example, the definition forevent 2 (287-2) is a dragging on a displayed object. The dragging, forexample, comprises a touch (or contact) on the displayed object for apredetermined phase, a movement of the touch across touch-sensitivedisplay 212, and liftoff of the touch (touch end). In some embodiments,the event also includes information for one or more associated eventhandlers 290.

In some embodiments, event definition 287 includes a definition of anevent for a respective user-interface object. In some embodiments, eventcomparator 284 performs a hit test to determine which user-interfaceobject is associated with a sub-event. For example, in an applicationview in which three user-interface objects are displayed ontouch-sensitive display 212, when a touch is detected on touch-sensitivedisplay 212, event comparator 284 performs a hit test to determine whichof the three user-interface objects is associated with the touch(sub-event). If each displayed object is associated with a respectiveevent handler 290, the event comparator uses the result of the hit testto determine which event handler 290 should be activated. For example,event comparator 284 selects an event handler associated with thesub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) alsoincludes delayed actions that delay delivery of the event informationuntil after it has been determined whether the sequence of sub-eventsdoes or does not correspond to the event recognizer's event type.

When a respective event recognizer 280 determines that the series ofsub-events do not match any of the events in event definitions 286, therespective event recognizer 280 enters an event impossible, eventfailed, or event ended state, after which it disregards subsequentsub-events of the touch-based gesture. In this situation, other eventrecognizers, if any, that remain active for the hit view continue totrack and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 280 includes metadata283 with configurable properties, flags, and/or lists that indicate howthe event delivery system should perform sub-event delivery to activelyinvolved event recognizers. In some embodiments, metadata 283 includesconfigurable properties, flags, and/or lists that indicate how eventrecognizers interact, or are enabled to interact, with one another. Insome embodiments, metadata 283 includes configurable properties, flags,and/or lists that indicate whether sub-events are delivered to varyinglevels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 280 activates eventhandler 290 associated with an event when one or more particularsub-events of an event are recognized. In some embodiments, a respectiveevent recognizer 280 delivers event information associated with theevent to event handler 290. Activating an event handler 290 is distinctfrom sending (and deferred sending) sub-events to a respective hit view.In some embodiments, event recognizer 280 throws a flag associated withthe recognized event, and event handler 290 associated with the flagcatches the flag and performs a predefined process.

In some embodiments, event delivery instructions 288 include sub-eventdelivery instructions that deliver event information about a sub-eventwithout activating an event handler. Instead, the sub-event deliveryinstructions deliver event information to event handlers associated withthe series of sub-events or to actively involved views. Event handlersassociated with the series of sub-events or with actively involved viewsreceive the event information and perform a predetermined process.

In some embodiments, data updater 276 creates and updates data used inapplication 236-1. For example, data updater 276 updates the telephonenumber used in contacts module 237, or stores a video file used in videoplayer module. In some embodiments, object updater 277 creates andupdates objects used in application 236-1. For example, object updater277 creates a new user-interface object or updates the position of auser-interface object. GUI updater 278 updates the GUI. For example, GUIupdater 278 prepares display information and sends it to graphics module232 for display on a touch-sensitive display.

In some embodiments, event handler(s) 290 includes or has access to dataupdater 276, object updater 277, and GUI updater 278. In someembodiments, data updater 276, object updater 277, and GUI updater 278are included in a single module of a respective application 236-1 orapplication view 291. In other embodiments, they are included in two ormore software modules.

It shall be understood that the foregoing discussion regarding eventhandling of user touches on touch-sensitive displays also applies toother forms of user inputs to operate multifunction devices 200 withinput devices, not all of which are initiated on touch screens. Forexample, mouse movement and mouse button presses, optionally coordinatedwith single or multiple keyboard presses or holds; contact movementssuch as taps, drags, scrolls, etc. on touchpads; pen stylus inputs;movement of the device; oral instructions; detected eye movements;biometric inputs; and/or any combination thereof are optionally utilizedas inputs corresponding to sub-events which define an event to berecognized.

FIG. 3 illustrates a portable multifunction device 200 having a touchscreen 212 in accordance with some embodiments. The touch screenoptionally displays one or more graphics within user interface (UI) 300.In this embodiment, as well as others described below, a user is enabledto select one or more of the graphics by making a gesture on thegraphics, for example, with one or more fingers 302 (not drawn to scalein the figure) or one or more styluses 303 (not drawn to scale in thefigure). In some embodiments, selection of one or more graphics occurswhen the user breaks contact with the one or more graphics. In someembodiments, the gesture optionally includes one or more taps, one ormore swipes (from left to right, right to left, upward and/or downward),and/or a rolling of a finger (from right to left, left to right, upwardand/or downward) that has made contact with device 200. In someimplementations or circumstances, inadvertent contact with a graphicdoes not select the graphic. For example, a swipe gesture that sweepsover an application icon optionally does not select the correspondingapplication when the gesture corresponding to selection is a tap.

Device 200 also includes one or more physical buttons, such as “home” ormenu button 304. As described previously, menu button 304 is used tonavigate to any application 236 in a set of applications that isexecuted on device 200. Alternatively, in some embodiments, the menubutton is implemented as a soft key in a GUI displayed on touch screen212.

In one embodiment, device 200 includes touch screen 212, menu button304, push button 306 for powering the device on/off and locking thedevice, volume adjustment button(s) 308, subscriber identity module(SIM) card slot 310, headset jack 312, and docking/charging externalport 224. Push button 306 is, optionally, used to turn the power on/offon the device by depressing the button and holding the button in thedepressed state for a predefined time interval; to lock the device bydepressing the button and releasing the button before the predefinedtime interval has elapsed; and/or to unlock the device or initiate anunlock process. In an alternative embodiment, device 200 also acceptsverbal input for activation or deactivation of some functions throughmicrophone 213. Device 200 also, optionally, includes one or morecontact intensity sensors 265 for detecting intensity of contacts ontouch screen 212 and/or one or more tactile output generators 267 forgenerating tactile outputs for a user of device 200.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments. Device 400 need not be portable. In some embodiments,device 400 is a laptop computer, a desktop computer, a tablet computer,a multimedia player device, a navigation device, an educational device(such as a child's learning toy), a gaming system, or a control device(e.g., a home or industrial controller). Device 400 typically includesone or more processing units (CPUs) 410, one or more network or othercommunications interfaces 460, memory 470, and one or more communicationbuses 420 for interconnecting these components. Communication buses 420optionally include circuitry (sometimes called a chipset) thatinterconnects and controls communications between system components.Device 400 includes input/output (I/O) interface 430 comprising display440, which is typically a touch screen display. I/O interface 430 alsooptionally includes a keyboard and/or mouse (or other pointing device)450 and touchpad 455, tactile output generator 457 for generatingtactile outputs on device 400 (e.g., similar to tactile outputgenerator(s) 267 described above with reference to FIG. 2A), sensors 459(e.g., optical, acceleration, proximity, touch-sensitive, and/or contactintensity sensors similar to contact intensity sensor(s) 265 describedabove with reference to FIG. 2A). Memory 470 includes high-speed randomaccess memory, such as DRAM, SRAM, DDR RAM, or other random access solidstate memory devices; and optionally includes non-volatile memory, suchas one or more magnetic disk storage devices, optical disk storagedevices, flash memory devices, or other non-volatile solid state storagedevices. Memory 470 optionally includes one or more storage devicesremotely located from CPU(s) 410. In some embodiments, memory 470 storesprograms, modules, and data structures analogous to the programs,modules, and data structures stored in memory 202 of portablemultifunction device 200 (FIG. 2A), or a subset thereof. Furthermore,memory 470 optionally stores additional programs, modules, and datastructures not present in memory 202 of portable multifunction device200. For example, memory 470 of device 400 optionally stores drawingmodule 480, presentation module 482, word processing module 484, websitecreation module 486, disk authoring module 488, and/or spreadsheetmodule 490, while memory 202 of portable multifunction device 200 (FIG.2A) optionally does not store these modules.

Each of the above-identified elements in FIG. 4 is, in some examples,stored in one or more of the previously mentioned memory devices. Eachof the above-identified modules corresponds to a set of instructions forperforming a function described above. The above-identified modules orprograms (e.g., sets of instructions) need not be implemented asseparate software programs, procedures, or modules, and thus varioussubsets of these modules are combined or otherwise rearranged in variousembodiments. In some embodiments, memory 470 stores a subset of themodules and data structures identified above. Furthermore, memory 470stores additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces thatcan be implemented on, for example, portable multifunction device 200.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on portable multifunction device 200 in accordance withsome embodiments. Similar user interfaces are implemented on device 400.In some embodiments, user interface 500 includes the following elements,or a subset or superset thereof:

Signal strength indicator(s) 502 for wireless communication(s), such ascellular and Wi-Fi signals;

-   -   Time 504;    -   Bluetooth indicator 505;    -   Battery status indicator 506;    -   Tray 508 with icons for frequently used applications, such as:        -   Icon 516 for telephone module 238, labeled “Phone,” which            optionally includes an indicator 514 of the number of missed            calls or voicemail messages;        -   Icon 518 for e-mail client module 240, labeled “Mail,” which            optionally includes an indicator 510 of the number of unread            e-mails;        -   Icon 520 for browser module 247, labeled “Browser;” and        -   Icon 522 for video and music player module 252, also            referred to as iPod (trademark of Apple Inc.) module 252,            labeled “iPod;” and    -   Icons for other applications, such as:        -   Icon 524 for IM module 241, labeled “Messages;”        -   Icon 526 for calendar module 248, labeled “Calendar;”        -   Icon 528 for image management module 244, labeled “Photos;”        -   Icon 530 for camera module 243, labeled “Camera;”        -   Icon 532 for online video module 255, labeled “Online            Video;”        -   Icon 534 for stocks widget 249-2, labeled “Stocks;”        -   Icon 536 for map module 254, labeled “Maps;”        -   Icon 538 for weather widget 249-1, labeled “Weather;”        -   Icon 540 for alarm clock widget 249-4, labeled “Clock;”        -   Icon 542 for workout support module 242, labeled “Workout            Support;”        -   Icon 544 for notes module 253, labeled “Notes;” and        -   Icon 546 for a settings application or module, labeled            “Settings,” which provides access to settings for device 200            and its various applications 236.

It should be noted that the icon labels illustrated in FIG. 5A aremerely exemplary. For example, icon 522 for video and music playermodule 252 is optionally labeled “Music” or “Music Player.” Other labelsare, optionally, used for various application icons. In someembodiments, a label for a respective application icon includes a nameof an application corresponding to the respective application icon. Insome embodiments, a label for a particular application icon is distinctfrom a name of an application corresponding to the particularapplication icon.

FIG. 5B illustrates an exemplary user interface on a device (e.g.,device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tabletor touchpad 455, FIG. 4) that is separate from the display 550 (e.g.,touch screen display 212). Device 400 also, optionally, includes one ormore contact intensity sensors (e.g., one or more of sensors 457) fordetecting intensity of contacts on touch-sensitive surface 551 and/orone or more tactile output generators 459 for generating tactile outputsfor a user of device 400.

Although some of the examples which follow will be given with referenceto inputs on touch screen display 212 (where the touch-sensitive surfaceand the display are combined), in some embodiments, the device detectsinputs on a touch-sensitive surface that is separate from the display,as shown in FIG. 5B. In some embodiments, the touch-sensitive surface(e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) thatcorresponds to a primary axis (e.g., 553 in FIG. 5B) on the display(e.g., 550). In accordance with these embodiments, the device detectscontacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface551 at locations that correspond to respective locations on the display(e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570).In this way, user inputs (e.g., contacts 560 and 562, and movementsthereof) detected by the device on the touch-sensitive surface (e.g.,551 in FIG. 5B) are used by the device to manipulate the user interfaceon the display (e.g., 550 in FIG. 5B) of the multifunction device whenthe touch-sensitive surface is separate from the display. It should beunderstood that similar methods are, optionally, used for other userinterfaces described herein.

Additionally, while the following examples are given primarily withreference to finger inputs (e.g., finger contacts, finger tap gestures,finger swipe gestures), it should be understood that, in someembodiments, one or more of the finger inputs are replaced with inputfrom another input device (e.g., a mouse-based input or stylus input).For example, a swipe gesture is, optionally, replaced with a mouse click(e.g., instead of a contact) followed by movement of the cursor alongthe path of the swipe (e.g., instead of movement of the contact). Asanother example, a tap gesture is, optionally, replaced with a mouseclick while the cursor is located over the location of the tap gesture(e.g., instead of detection of the contact followed by ceasing to detectthe contact). Similarly, when multiple user inputs are simultaneouslydetected, it should be understood that multiple computer mice are,optionally, used simultaneously, or a mouse and finger contacts are,optionally, used simultaneously.

FIG. 6A illustrates exemplary personal electronic device 600. Device 600includes body 602. In some embodiments, device 600 includes some or allof the features described with respect to devices 200 and 400 (e.g.,FIGS. 2A-4B). In some embodiments, device 600 has touch-sensitivedisplay screen 604, hereafter touch screen 604. Alternatively, or inaddition to touch screen 604, device 600 has a display and atouch-sensitive surface. As with devices 200 and 400, in someembodiments, touch screen 604 (or the touch-sensitive surface) has oneor more intensity sensors for detecting intensity of contacts (e.g.,touches) being applied. The one or more intensity sensors of touchscreen 604 (or the touch-sensitive surface) provide output data thatrepresents the intensity of touches. The user interface of device 600responds to touches based on their intensity, meaning that touches ofdifferent intensities can invoke different user interface operations ondevice 600.

Techniques for detecting and processing touch intensity are found, forexample, in related applications: International Patent ApplicationSerial No. PCT/US2013/040061, titled “Device, Method, and Graphical UserInterface for Displaying User Interface Objects Corresponding to anApplication,” filed May 8, 2013, and International Patent ApplicationSerial No. PCT/US2013/069483, titled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” filed Nov. 11, 2013, each of which is herebyincorporated by reference in their entirety.

In some embodiments, device 600 has one or more input mechanisms 606 and608. Input mechanisms 606 and 608, if included, are physical. Examplesof physical input mechanisms include push buttons and rotatablemechanisms. In some embodiments, device 600 has one or more attachmentmechanisms. Such attachment mechanisms, if included, can permitattachment of device 600 with, for example, hats, eyewear, earrings,necklaces, shirts, jackets, bracelets, watch straps, chains, trousers,belts, shoes, purses, backpacks, and so forth. These attachmentmechanisms permit device 600 to be worn by a user.

FIG. 6B depicts exemplary personal electronic device 600. In someembodiments, device 600 includes some or all of the components describedwith respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612 thatoperatively couples I/O section 614 with one or more computer processors616 and memory 618. I/O section 614 is connected to display 604, whichcan have touch-sensitive component 622 and, optionally, touch-intensitysensitive component 624. In addition, I/O section 614 is connected withcommunication unit 630 for receiving application and operating systemdata, using Wi-Fi, Bluetooth, near field communication (NFC), cellular,and/or other wireless communication techniques. Device 600 includesinput mechanisms 606 and/or 608. Input mechanism 606 is a rotatableinput device or a depressible and rotatable input device, for example.Input mechanism 608 is a button, in some examples.

Input mechanism 608 is a microphone, in some examples. Personalelectronic device 600 includes, for example, various sensors, such asGPS sensor 632, accelerometer 634, directional sensor 640 (e.g.,compass), gyroscope 636, motion sensor 638, and/or a combinationthereof, all of which are operatively connected to I/O section 614.

Memory 618 of personal electronic device 600 is a non-transitorycomputer-readable storage medium, for storing computer-executableinstructions, which, when executed by one or more computer processors616, for example, cause the computer processors to perform thetechniques and processes described below. The computer-executableinstructions, for example, are also stored and/or transported within anynon-transitory computer-readable storage medium for use by or inconnection with an instruction execution system, apparatus, or device,such as a computer-based system, processor-containing system, or othersystem that can fetch the instructions from the instruction executionsystem, apparatus, or device and execute the instructions. Personalelectronic device 600 is not limited to the components and configurationof FIG. 6B, but can include other or additional components in multipleconfigurations.

As used here, the term “affordance” refers to a user-interactivegraphical user interface object that is, for example, displayed on thedisplay screen of devices 200, 400, and/or 600 (FIGS. 2, 4, and 6). Forexample, an image (e.g., icon), a button, and text (e.g., hyperlink)each constitutes an affordance.

As used herein, the term “focus selector” refers to an input elementthat indicates a current part of a user interface with which a user isinteracting. In some implementations that include a cursor or otherlocation marker, the cursor acts as a “focus selector” so that when aninput (e.g., a press input) is detected on a touch-sensitive surface(e.g., touchpad 455 in FIG. 4 or touch-sensitive surface 551 in FIG. 5B)while the cursor is over a particular user interface element (e.g., abutton, window, slider or other user interface element), the particularuser interface element is adjusted in accordance with the detectedinput. In some implementations that include a touch screen display(e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212in FIG. 5A) that enables direct interaction with user interface elementson the touch screen display, a detected contact on the touch screen actsas a “focus selector” so that when an input (e.g., a press input by thecontact) is detected on the touch screen display at a location of aparticular user interface element (e.g., a button, window, slider, orother user interface element), the particular user interface element isadjusted in accordance with the detected input. In some implementations,focus is moved from one region of a user interface to another region ofthe user interface without corresponding movement of a cursor ormovement of a contact on a touch screen display (e.g., by using a tabkey or arrow keys to move focus from one button to another button); inthese implementations, the focus selector moves in accordance withmovement of focus between different regions of the user interface.Without regard to the specific form taken by the focus selector, thefocus selector is generally the user interface element (or contact on atouch screen display) that is controlled by the user so as tocommunicate the user's intended interaction with the user interface(e.g., by indicating, to the device, the element of the user interfacewith which the user is intending to interact). For example, the locationof a focus selector (e.g., a cursor, a contact, or a selection box) overa respective button while a press input is detected on thetouch-sensitive surface (e.g., a touchpad or touch screen) will indicatethat the user is intending to activate the respective button (as opposedto other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristicintensity” of a contact refers to a characteristic of the contact basedon one or more intensities of the contact. In some embodiments, thecharacteristic intensity is based on multiple intensity samples. Thecharacteristic intensity is, optionally, based on a predefined number ofintensity samples, or a set of intensity samples collected during apredetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10seconds) relative to a predefined event (e.g., after detecting thecontact, prior to detecting liftoff of the contact, before or afterdetecting a start of movement of the contact, prior to detecting an endof the contact, before or after detecting an increase in intensity ofthe contact, and/or before or after detecting a decrease in intensity ofthe contact). A characteristic intensity of a contact is, optionallybased on one or more of: a maximum value of the intensities of thecontact, a mean value of the intensities of the contact, an averagevalue of the intensities of the contact, a top 10 percentile value ofthe intensities of the contact, a value at the half maximum of theintensities of the contact, a value at the 90 percent maximum of theintensities of the contact, or the like. In some embodiments, theduration of the contact is used in determining the characteristicintensity (e.g., when the characteristic intensity is an average of theintensity of the contact over time). In some embodiments, thecharacteristic intensity is compared to a set of one or more intensitythresholds to determine whether an operation has been performed by auser. For example, the set of one or more intensity thresholds includesa first intensity threshold and a second intensity threshold. In thisexample, a contact with a characteristic intensity that does not exceedthe first threshold results in a first operation, a contact with acharacteristic intensity that exceeds the first intensity threshold anddoes not exceed the second intensity threshold results in a secondoperation, and a contact with a characteristic intensity that exceedsthe second threshold results in a third operation. In some embodiments,a comparison between the characteristic intensity and one or morethresholds is used to determine whether or not to perform one or moreoperations (e.g., whether to perform a respective operation or forgoperforming the respective operation) rather than being used to determinewhether to perform a first operation or a second operation.

In some embodiments, a portion of a gesture is identified for purposesof determining a characteristic intensity. For example, atouch-sensitive surface receives a continuous swipe contacttransitioning from a start location and reaching an end location, atwhich point the intensity of the contact increases. In this example, thecharacteristic intensity of the contact at the end location is based ononly a portion of the continuous swipe contact, and not the entire swipecontact (e.g., only the portion of the swipe contact at the endlocation). In some embodiments, a smoothing algorithm is applied to theintensities of the swipe contact prior to determining the characteristicintensity of the contact. For example, the smoothing algorithmoptionally includes one or more of: an unweighted sliding-averagesmoothing algorithm, a triangular smoothing algorithm, a median filtersmoothing algorithm, and/or an exponential smoothing algorithm. In somecircumstances, these smoothing algorithms eliminate narrow spikes ordips in the intensities of the swipe contact for purposes of determininga characteristic intensity.

The intensity of a contact on the touch-sensitive surface ischaracterized relative to one or more intensity thresholds, such as acontact-detection intensity threshold, a light press intensitythreshold, a deep press intensity threshold, and/or one or more otherintensity thresholds. In some embodiments, the light press intensitythreshold corresponds to an intensity at which the device will performoperations typically associated with clicking a button of a physicalmouse or a trackpad. In some embodiments, the deep press intensitythreshold corresponds to an intensity at which the device will performoperations that are different from operations typically associated withclicking a button of a physical mouse or a trackpad. In someembodiments, when a contact is detected with a characteristic intensitybelow the light press intensity threshold (e.g., and above a nominalcontact-detection intensity threshold below which the contact is nolonger detected), the device will move a focus selector in accordancewith movement of the contact on the touch-sensitive surface withoutperforming an operation associated with the light press intensitythreshold or the deep press intensity threshold. Generally, unlessotherwise stated, these intensity thresholds are consistent betweendifferent sets of user interface figures.

An increase of characteristic intensity of the contact from an intensitybelow the light press intensity threshold to an intensity between thelight press intensity threshold and the deep press intensity thresholdis sometimes referred to as a “light press” input. An increase ofcharacteristic intensity of the contact from an intensity below the deeppress intensity threshold to an intensity above the deep press intensitythreshold is sometimes referred to as a “deep press” input. An increaseof characteristic intensity of the contact from an intensity below thecontact-detection intensity threshold to an intensity between thecontact-detection intensity threshold and the light press intensitythreshold is sometimes referred to as detecting the contact on thetouch-surface. A decrease of characteristic intensity of the contactfrom an intensity above the contact-detection intensity threshold to anintensity below the contact-detection intensity threshold is sometimesreferred to as detecting liftoff of the contact from the touch-surface.In some embodiments, the contact-detection intensity threshold is zero.In some embodiments, the contact-detection intensity threshold isgreater than zero.

In some embodiments described herein, one or more operations areperformed in response to detecting a gesture that includes a respectivepress input or in response to detecting the respective press inputperformed with a respective contact (or a plurality of contacts), wherethe respective press input is detected based at least in part ondetecting an increase in intensity of the contact (or plurality ofcontacts) above a press-input intensity threshold. In some embodiments,the respective operation is performed in response to detecting theincrease in intensity of the respective contact above the press-inputintensity threshold (e.g., a “down stroke” of the respective pressinput). In some embodiments, the press input includes an increase inintensity of the respective contact above the press-input intensitythreshold and a subsequent decrease in intensity of the contact belowthe press-input intensity threshold, and the respective operation isperformed in response to detecting the subsequent decrease in intensityof the respective contact below the press-input threshold (e.g., an “upstroke” of the respective press input).

In some embodiments, the device employs intensity hysteresis to avoidaccidental inputs sometimes termed “jitter,” where the device defines orselects a hysteresis intensity threshold with a predefined relationshipto the press-input intensity threshold (e.g., the hysteresis intensitythreshold is X intensity units lower than the press-input intensitythreshold or the hysteresis intensity threshold is 75%, 90%, or somereasonable proportion of the press-input intensity threshold). Thus, insome embodiments, the press input includes an increase in intensity ofthe respective contact above the press-input intensity threshold and asubsequent decrease in intensity of the contact below the hysteresisintensity threshold that corresponds to the press-input intensitythreshold, and the respective operation is performed in response todetecting the subsequent decrease in intensity of the respective contactbelow the hysteresis intensity threshold (e.g., an “up stroke” of therespective press input). Similarly, in some embodiments, the press inputis detected only when the device detects an increase in intensity of thecontact from an intensity at or below the hysteresis intensity thresholdto an intensity at or above the press-input intensity threshold and,optionally, a subsequent decrease in intensity of the contact to anintensity at or below the hysteresis intensity, and the respectiveoperation is performed in response to detecting the press input (e.g.,the increase in intensity of the contact or the decrease in intensity ofthe contact, depending on the circumstances).

For ease of explanation, the descriptions of operations performed inresponse to a press input associated with a press-input intensitythreshold or in response to a gesture including the press input are,optionally, triggered in response to detecting either: an increase inintensity of a contact above the press-input intensity threshold, anincrease in intensity of a contact from an intensity below thehysteresis intensity threshold to an intensity above the press-inputintensity threshold, a decrease in intensity of the contact below thepress-input intensity threshold, and/or a decrease in intensity of thecontact below the hysteresis intensity threshold corresponding to thepress-input intensity threshold. Additionally, in examples where anoperation is described as being performed in response to detecting adecrease in intensity of a contact below the press-input intensitythreshold, the operation is, optionally, performed in response todetecting a decrease in intensity of the contact below a hysteresisintensity threshold corresponding to, and lower than, the press-inputintensity threshold.

3. Digital Assistant System

FIG. 7A illustrates a block diagram of digital assistant system 700 inaccordance with various examples. In some examples, digital assistantsystem 700 is implemented on a standalone computer system. In someexamples, digital assistant system 700 is distributed across multiplecomputers. In some examples, some of the modules and functions of thedigital assistant are divided into a server portion and a clientportion, where the client portion resides on one or more user devices(e.g., devices 104, 122, 200, 400, or 600) and communicates with theserver portion (e.g., server system 108) through one or more networks,e.g., as shown in FIG. 1. In some examples, digital assistant system 700is an implementation of server system 108 (and/or DA server 106) shownin FIG. 1. It should be noted that digital assistant system 700 is onlyone example of a digital assistant system, and that digital assistantsystem 700 can have more or fewer components than shown, can combine twoor more components, or can have a different configuration or arrangementof the components. The various components shown in FIG. 7A areimplemented in hardware, software instructions for execution by one ormore processors, firmware, including one or more signal processingand/or application specific integrated circuits, or a combinationthereof.

Digital assistant system 700 includes memory 702, one or more processors704, input/output (I/O) interface 706, and network communicationsinterface 708. These components can communicate with one another overone or more communication buses or signal lines 710.

In some examples, memory 702 includes a non-transitory computer-readablemedium, such as high-speed random access memory and/or a non-volatilecomputer-readable storage medium (e.g., one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices).

In some examples, I/O interface 706 couples input/output devices 716 ofdigital assistant system 700, such as displays, keyboards, touchscreens, and microphones, to user interface module 722. I/O interface706, in conjunction with user interface module 722, receives user inputs(e.g., voice input, keyboard inputs, touch inputs, etc.) and processesthem accordingly. In some examples, e.g., when the digital assistant isimplemented on a standalone user device, digital assistant system 700includes any of the components and I/O communication interfacesdescribed with respect to devices 200, 400, or 600 in FIGS. 2A, 4, 6A-B,respectively. In some examples, digital assistant system 700 representsthe server portion of a digital assistant implementation, and caninteract with the user through a client-side portion residing on a userdevice (e.g., devices 104, 200, 400, or 600).

In some examples, the network communications interface 708 includeswired communication port(s) 712 and/or wireless transmission andreception circuitry 714. The wired communication port(s) receives andsend communication signals via one or more wired interfaces, e.g.,Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wirelesscircuitry 714 receives and sends RF signals and/or optical signalsfrom/to communications networks and other communications devices. Thewireless communications use any of a plurality of communicationsstandards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA,Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communicationprotocol. Network communications interface 708 enables communicationbetween digital assistant system 700 with networks, such as theInternet, an intranet, and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN), and/or ametropolitan area network (MAN), and other devices.

In some examples, memory 702, or the computer-readable storage media ofmemory 702, stores programs, modules, instructions, and data structuresincluding all or a subset of: operating system 718, communicationsmodule 720, user interface module 722, one or more applications 724, anddigital assistant module 726. In particular, memory 702, or thecomputer-readable storage media of memory 702, stores instructions forperforming the processes described below. One or more processors 704execute these programs, modules, and instructions, and reads/writesfrom/to the data structures.

Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communications between varioushardware, firmware, and software components.

Communications module 720 facilitates communications between digitalassistant system 700 with other devices over network communicationsinterface 708. For example, communications module 720 communicates withRF circuitry 208 of electronic devices such as devices 200, 400, and 600shown in FIG. 2A, 4, 6A-B, respectively. Communications module 720 alsoincludes various components for handling data received by wirelesscircuitry 714 and/or wired communications port 712.

User interface module 722 receives commands and/or inputs from a uservia I/O interface 706 (e.g., from a keyboard, touch screen, pointingdevice, controller, and/or microphone), and generate user interfaceobjects on a display. User interface module 722 also prepares anddelivers outputs (e.g., speech, sound, animation, text, icons,vibrations, haptic feedback, light, etc.) to the user via the I/Ointerface 706 (e.g., through displays, audio channels, speakers,touch-pads, etc.).

Applications 724 include programs and/or modules that are configured tobe executed by one or more processors 704. For example, if the digitalassistant system is implemented on a standalone user device,applications 724 include user applications, such as games, a calendarapplication, a navigation application, or an email application. Ifdigital assistant system 700 is implemented on a server, applications724 include resource management applications, diagnostic applications,or scheduling applications, for example.

Memory 702 also stores digital assistant module 726 (or the serverportion of a digital assistant). In some examples, digital assistantmodule 726 includes the following sub-modules, or a subset or supersetthereof: input/output processing module 728, speech-to-text (STT)processing module 730, natural language processing module 732, dialogueflow processing module 734, task flow processing module 736, serviceprocessing module 738, and speech synthesis module 740. Each of thesemodules has access to one or more of the following systems or data andmodels of the digital assistant module 726, or a subset or supersetthereof: ontology 760, vocabulary index 744, user data 748, task flowmodels 754, service models 756, and ASR systems.

In some examples, using the processing modules, data, and modelsimplemented in digital assistant module 726, the digital assistant canperform at least some of the following: converting speech input intotext; identifying a user's intent expressed in a natural language inputreceived from the user; actively eliciting and obtaining informationneeded to fully infer the user's intent (e.g., by disambiguating words,games, intentions, etc.); determining the task flow for fulfilling theinferred intent; and executing the task flow to fulfill the inferredintent.

In some examples, as shown in FIG. 7B, I/O processing module 728interacts with the user through I/O devices 716 in FIG. 7A or with auser device (e.g., devices 104, 200, 400, or 600) through networkcommunications interface 708 in FIG. 7A to obtain user input (e.g., aspeech input) and to provide responses (e.g., as speech outputs) to theuser input. I/O processing module 728 optionally obtains contextualinformation associated with the user input from the user device, alongwith or shortly after the receipt of the user input. The contextualinformation includes user-specific data, vocabulary, and/or preferencesrelevant to the user input. In some examples, the contextual informationalso includes software and hardware states of the user device at thetime the user request is received, and/or information related to thesurrounding environment of the user at the time that the user requestwas received. In some examples, I/O processing module 728 also sendsfollow-up questions to, and receive answers from, the user regarding theuser request. When a user request is received by I/O processing module728 and the user request includes speech input, I/O processing module728 forwards the speech input to STT processing module 730 (or speechrecognizer) for speech-to-text conversions.

STT processing module 730 includes one or more ASR systems. The one ormore ASR systems can process the speech input that is received throughI/O processing module 728 to produce a recognition result. Each ASRsystem includes a front-end speech pre-processor. The front-end speechpre-processor extracts representative features from the speech input.For example, the front-end speech pre-processor performs a Fouriertransform on the speech input to extract spectral features thatcharacterize the speech input as a sequence of representativemulti-dimensional vectors. Further, each ASR system includes one or morespeech recognition models (e.g., acoustic models and/or language models)and implements one or more speech recognition engines. Examples ofspeech recognition models include Hidden Markov Models, Gaussian-MixtureModels, Deep Neural Network Models, n-gram language models, and otherstatistical models. Examples of speech recognition engines include thedynamic time warping based engines and weighted finite-state transducers(WFST) based engines. The one or more speech recognition models and theone or more speech recognition engines are used to process the extractedrepresentative features of the front-end speech pre-processor to produceintermediate recognitions results (e.g., phonemes, phonemic strings, andsub-words), and ultimately, text recognition results (e.g., words, wordstrings, or sequence of tokens). In some examples, the speech input isprocessed at least partially by a third-party service or on the user'sdevice (e.g., device 104, 200, 400, or 600) to produce the recognitionresult. Once STT processing module 730 produces recognition resultscontaining a text string (e.g., words, or sequence of words, or sequenceof tokens), the recognition result is passed to natural languageprocessing module 732 for intent deduction.

More details on the speech-to-text processing are described in U.S.Utility application Ser. No. 13/236,942 for “Consolidating SpeechRecognition Results,” filed on Sep. 20, 2011, the entire disclosure ofwhich is incorporated herein by reference.

In some examples, STT processing module 730 includes and/or accesses avocabulary of recognizable words via phonetic alphabet conversion module731. Each vocabulary word is associated with one or more candidatepronunciations of the word represented in a speech recognition phoneticalphabet. In particular, the vocabulary of recognizable words includes aword that is associated with a plurality of candidate pronunciations.For example, the vocabulary includes the word “tomato” that isassociated with the candidate pronunciations of /

/ and /

/. Further, vocabulary words are associated with custom candidatepronunciations that are based on previous speech inputs from the user.Such custom candidate pronunciations are stored in STT processing module730 and are associated with a particular user via the user's profile onthe device. In some examples, the candidate pronunciations for words aredetermined based on the spelling of the word and one or more linguisticand/or phonetic rules. In some examples, the candidate pronunciationsare manually generated, e.g., based on known canonical pronunciations.

In some examples, the candidate pronunciations are ranked based on thecommonness of the candidate pronunciation. For example, the candidatepronunciation /

/ is ranked higher than /

/, because the former is a more commonly used pronunciation (e.g., amongall users, for users in a particular geographical region, or for anyother appropriate subset of users). In some examples, candidatepronunciations are ranked based on whether the candidate pronunciationis a custom candidate pronunciation associated with the user. Forexample, custom candidate pronunciations are ranked higher thancanonical candidate pronunciations. This can be useful for recognizingproper nouns having a unique pronunciation that deviates from canonicalpronunciation. In some examples, candidate pronunciations are associatedwith one or more speech characteristics, such as geographic origin,nationality, or ethnicity. For example, the candidate pronunciation /

/ is associated with the United States, whereas the candidatepronunciation /

/ is associated with Great Britain. Further, the rank of the candidatepronunciation is based on one or more characteristics (e.g., geographicorigin, nationality, ethnicity, etc.) of the user stored in the user'sprofile on the device. For example, it can be determined from the user'sprofile that the user is associated with the United States. Based on theuser being associated with the United States, the candidatepronunciation /

/ (associated with the United States) is ranked higher than thecandidate pronunciation /

/ (associated with Great Britain). In some examples, one of the rankedcandidate pronunciations is selected as a predicted pronunciation (e.g.,the most likely pronunciation).

When a speech input is received, STT processing module 730 is used todetermine the phonemes corresponding to the speech input (e.g., using anacoustic model), and then attempt to determine words that match thephonemes (e.g., using a language model). For example, if STT processingmodule 730 first identifies the sequence of phonemes /

/ corresponding to a portion of the speech input, it can then determine,based on vocabulary index 744, that this sequence corresponds to theword “tomato.”

In some examples, STT processing module 730 uses approximate matchingtechniques to determine words in an utterance. Thus, for example, theSTT processing module 730 determines that the sequence of phonemes /

/ corresponds to the word “tomato,” even if that particular sequence ofphonemes is not one of the candidate sequence of phonemes for that word.

Natural language processing module 732 (“natural language processor”) ofthe digital assistant takes the sequence of words or tokens (“tokensequence”) generated by STT processing module 730, and attempt toassociate the token sequence with one or more “actionable intents”recognized by the digital assistant. An “actionable intent” represents atask that can be performed by the digital assistant, and can have anassociated task flow implemented in task flow models 754. The associatedtask flow is a series of programmed actions and steps that the digitalassistant takes in order to perform the task. The scope of a digitalassistant's capabilities is dependent on the number and variety of taskflows that have been implemented and stored in task flow models 754, orin other words, on the number and variety of “actionable intents” thatthe digital assistant recognizes. The effectiveness of the digitalassistant, however, also dependents on the assistant's ability to inferthe correct “actionable intent(s)” from the user request expressed innatural language.

In some examples, in addition to the sequence of words or tokensobtained from STT processing module 730, natural language processingmodule 732 also receives contextual information associated with the userrequest, e.g., from I/O processing module 728. The natural languageprocessing module 732 optionally uses the contextual information toclarify, supplement, and/or further define the information contained inthe token sequence received from STT processing module 730. Thecontextual information includes, for example, user preferences,hardware, and/or software states of the user device, sensor informationcollected before, during, or shortly after the user request, priorinteractions (e.g., dialogue) between the digital assistant and theuser, and the like. As described herein, contextual information is, insome examples, dynamic, and changes with time, location, content of thedialogue, and other factors.

In some examples, the natural language processing is based on, e.g.,ontology 760. Ontology 760 is a hierarchical structure containing manynodes, each node representing either an “actionable intent” or a“property” relevant to one or more of the “actionable intents” or other“properties.” As noted above, an “actionable intent” represents a taskthat the digital assistant is capable of performing, i.e., it is“actionable” or can be acted on. A “property” represents a parameterassociated with an actionable intent or a sub-aspect of anotherproperty. A linkage between an actionable intent node and a propertynode in ontology 760 defines how a parameter represented by the propertynode pertains to the task represented by the actionable intent node.

In some examples, ontology 760 is made up of actionable intent nodes andproperty nodes. Within ontology 760, each actionable intent node islinked to one or more property nodes either directly or through one ormore intermediate property nodes. Similarly, each property node islinked to one or more actionable intent nodes either directly or throughone or more intermediate property nodes. For example, as shown in FIG.7C, ontology 760 includes a “restaurant reservation” node (i.e., anactionable intent node). Property nodes “restaurant,” “date/time” (forthe reservation), and “party size” are each directly linked to theactionable intent node (i.e., the “restaurant reservation” node).

In addition, property nodes “cuisine,” “price range,” “phone number,”and “location” are sub-nodes of the property node “restaurant,” and areeach linked to the “restaurant reservation” node (i.e., the actionableintent node) through the intermediate property node “restaurant.” Foranother example, as shown in FIG. 7C, ontology 760 also includes a “setreminder” node (i.e., another actionable intent node). Property nodes“date/time” (for setting the reminder) and “subject” (for the reminder)are each linked to the “set reminder” node. Since the property“date/time” is relevant to both the task of making a restaurantreservation and the task of setting a reminder, the property node“date/time” is linked to both the “restaurant reservation” node and the“set reminder” node in ontology 760.

An actionable intent node, along with its linked concept nodes, isdescribed as a “domain.” In the present discussion, each domain isassociated with a respective actionable intent, and refers to the groupof nodes (and the relationships there between) associated with theparticular actionable intent. For example, ontology 760 shown in FIG. 7Cincludes an example of restaurant reservation domain 762 and an exampleof reminder domain 764 within ontology 760. The restaurant reservationdomain includes the actionable intent node “restaurant reservation,”property nodes “restaurant,” “date/time,” and “party size,” andsub-property nodes “cuisine,” “price range,” “phone number,” and“location.” Reminder domain 764 includes the actionable intent node “setreminder,” and property nodes “subject” and “date/time.” In someexamples, ontology 760 is made up of many domains. Each domain sharesone or more property nodes with one or more other domains. For example,the “date/time” property node is associated with many different domains(e.g., a scheduling domain, a travel reservation domain, a movie ticketdomain, etc.), in addition to restaurant reservation domain 762 andreminder domain 764.

While FIG. 7C illustrates two example domains within ontology 760, otherdomains include, for example, “find a movie,” “initiate a phone call,”“find directions,” “schedule a meeting,” “send a message,” and “providean answer to a question,” “read a list,” “providing navigationinstructions,” “provide instructions for a task” and so on. A “send amessage” domain is associated with a “send a message” actionable intentnode, and further includes property nodes such as “recipient(s),”“message type,” and “message body.” The property node “recipient” isfurther defined, for example, by the sub-property nodes such as“recipient name” and “message address.”

In some examples, ontology 760 includes all the domains (and henceactionable intents) that the digital assistant is capable ofunderstanding and acting upon. In some examples, ontology 760 ismodified, such as by adding or removing entire domains or nodes, or bymodifying relationships between the nodes within the ontology 760.

In some examples, nodes associated with multiple related actionableintents are clustered under a “super domain” in ontology 760. Forexample, a “travel” super-domain includes a cluster of property nodesand actionable intent nodes related to travel. The actionable intentnodes related to travel includes “airline reservation,” “hotelreservation,” “car rental,” “get directions,” “find points of interest,”and so on. The actionable intent nodes under the same super domain(e.g., the “travel” super domain) have many property nodes in common.For example, the actionable intent nodes for “airline reservation,”“hotel reservation,” “car rental,” “get directions,” and “find points ofinterest” share one or more of the property nodes “start location,”“destination,” “departure date/time,” “arrival date/time,” and “partysize.”

In some examples, each node in ontology 760 is associated with a set ofwords and/or phrases that are relevant to the property or actionableintent represented by the node. The respective set of words and/orphrases associated with each node are the so-called “vocabulary”associated with the node. The respective set of words and/or phrasesassociated with each node are stored in vocabulary index 744 inassociation with the property or actionable intent represented by thenode. For example, returning to FIG. 7B, the vocabulary associated withthe node for the property of “restaurant” includes words such as “food,”“drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” andso on. For another example, the vocabulary associated with the node forthe actionable intent of “initiate a phone call” includes words andphrases such as “call,” “phone,” “dial,” “ring,” “call this number,”“make a call to,” and so on. The vocabulary index 744 optionallyincludes words and phrases in different languages.

Natural language processing module 732 receives the token sequence(e.g., a text string) from STT processing module 730, and determineswhat nodes are implicated by the words in the token sequence. In someexamples, if a word or phrase in the token sequence is found to beassociated with one or more nodes in ontology 760 (via vocabulary index744), the word or phrase “triggers” or “activates” those nodes. Based onthe quantity and/or relative importance of the activated nodes, naturallanguage processing module 732 selects one of the actionable intents asthe task that the user intended the digital assistant to perform. Insome examples, the domain that has the most “triggered” nodes isselected. In some examples, the domain having the highest confidencevalue (e.g., based on the relative importance of its various triggerednodes) is selected. In some examples, the domain is selected based on acombination of the number and the importance of the triggered nodes. Insome examples, additional factors are considered in selecting the nodeas well, such as whether the digital assistant has previously correctlyinterpreted a similar request from a user.

User data 748 includes user-specific information, such as user-specificvocabulary, user preferences, user address, user's default and secondarylanguages, user's contact list, and other short-term or long-terminformation for each user. In some examples, natural language processingmodule 732 uses the user-specific information to supplement theinformation contained in the user input to further define the userintent. For example, for a user request “invite my friends to mybirthday party,” natural language processing module 732 is able toaccess user data 748 to determine who the “friends” are and when andwhere the “birthday party” would be held, rather than requiring the userto provide such information explicitly in his/her request.

Other details of searching an ontology based on a token string isdescribed in U.S. Utility application Ser. No. 12/341,743 for “Methodand Apparatus for Searching Using An Active Ontology,” filed Dec. 22,2008, the entire disclosure of which is incorporated herein byreference.

In some examples, once natural language processing module 732 identifiesan actionable intent (or domain) based on the user request, naturallanguage processing module 732 generates a structured query to representthe identified actionable intent. In some examples, the structured queryincludes parameters for one or more nodes within the domain for theactionable intent, and at least some of the parameters are populatedwith the specific information and requirements specified in the userrequest. For example, the user says “Make me a dinner reservation at asushi place at 7.” In this case, natural language processing module 732is able to correctly identify the actionable intent to be “restaurantreservation” based on the user input. According to the ontology, astructured query for a “restaurant reservation” domain includesparameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and thelike. In some examples, based on the speech input and the text derivedfrom the speech input using STT processing module 730, natural languageprocessing module 732 generates a partial structured query for therestaurant reservation domain, where the partial structured queryincludes the parameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, inthis example, the user's utterance contains insufficient information tocomplete the structured query associated with the domain. Therefore,other necessary parameters such as {Party Size} and {Date} is notspecified in the structured query based on the information currentlyavailable. In some examples, natural language processing module 732populates some parameters of the structured query with receivedcontextual information. For example, in some examples, if the userrequested a sushi restaurant “near me,” natural language processingmodule 732 populates a {location} parameter in the structured query withGPS coordinates from the user device.

In some examples, natural language processing module 732 passes thegenerated structured query (including any completed parameters) to taskflow processing module 736 (“task flow processor”). Task flow processingmodule 736 is configured to receive the structured query from naturallanguage processing module 732, complete the structured query, ifnecessary, and perform the actions required to “complete” the user'sultimate request. In some examples, the various procedures necessary tocomplete these tasks are provided in task flow models 754. In someexamples, task flow models 754 include procedures for obtainingadditional information from the user and task flows for performingactions associated with the actionable intent.

As described above, in order to complete a structured query, task flowprocessing module 736 needs to initiate additional dialogue with theuser in order to obtain additional information, and/or disambiguatepotentially ambiguous utterances. When such interactions are necessary,task flow processing module 736 invokes dialogue flow processing module734 to engage in a dialogue with the user. In some examples, dialogueflow processing module 734 determines how (and/or when) to ask the userfor the additional information and receives and processes the userresponses. The questions are provided to and answers are received fromthe users through I/O processing module 728. In some examples, dialogueflow processing module 734 presents dialogue output to the user viaaudio and/or visual output, and receives input from the user via spokenor physical (e.g., clicking) responses. Continuing with the exampleabove, when task flow processing module 736 invokes dialogue flowprocessing module 734 to determine the “party size” and “date”information for the structured query associated with the domain“restaurant reservation,” dialogue flow processing module 734 generatesquestions such as “For how many people?” and “On which day?” to pass tothe user. Once answers are received from the user, dialogue flowprocessing module 734 then populates the structured query with themissing information, or pass the information to task flow processingmodule 736 to complete the missing information from the structuredquery.

Once task flow processing module 736 has completed the structured queryfor an actionable intent, task flow processing module 736 proceeds toperform the ultimate task associated with the actionable intent.Accordingly, task flow processing module 736 executes the steps andinstructions in the task flow model according to the specific parameterscontained in the structured query. For example, the task flow model forthe actionable intent of “restaurant reservation” includes steps andinstructions for contacting a restaurant and actually requesting areservation for a particular party size at a particular time. Forexample, using a structured query such as: {restaurant reservation,restaurant=ABC Café, date=3/12/2012, time=7 pm, party size=5}, task flowprocessing module 736 performs the steps of: (1) logging onto a serverof the ABC Café or a restaurant reservation system such as OPENTABLE®,(2) entering the date, time, and party size information in a form on thewebsite, (3) submitting the form, and (4) making a calendar entry forthe reservation in the user's calendar.

In some examples, task flow processing module 736 employs the assistanceof service processing module 738 (“service processing module”) tocomplete a task requested in the user input or to provide aninformational answer requested in the user input. For example, serviceprocessing module 738 acts on behalf of task flow processing module 736to make a phone call, set a calendar entry, invoke a map search, invokeor interact with other user applications installed on the user device,and invoke or interact with third-party services (e.g., a restaurantreservation portal, a social networking website, a banking portal,etc.). In some examples, the protocols and application programminginterfaces (API) required by each service are specified by a respectiveservice model among service models 756. Service processing module 738accesses the appropriate service model for a service and generaterequests for the service in accordance with the protocols and APIsrequired by the service according to the service model.

For example, if a restaurant has enabled an online reservation service,the restaurant submits a service model specifying the necessaryparameters for making a reservation and the APIs for communicating thevalues of the necessary parameter to the online reservation service.When requested by task flow processing module 736, service processingmodule 738 establishes a network connection with the online reservationservice using the web address stored in the service model, and send thenecessary parameters of the reservation (e.g., time, date, party size)to the online reservation interface in a format according to the API ofthe online reservation service.

In some examples, natural language processing module 732, dialogue flowprocessing module 734, and task flow processing module 736 are usedcollectively and iteratively to infer and define the user's intent,obtain information to further clarify and refine the user intent, andfinally generate a response (i.e., an output to the user, or thecompletion of a task) to fulfill the user's intent. The generatedresponse is a dialogue response to the speech input that at leastpartially fulfills the user's intent. Further, in some examples, thegenerated response is output as a speech output. In these examples, thegenerated response is sent to speech synthesis module 740 (e.g., speechsynthesizer) where it can be processed to synthesize the dialogueresponse in speech form. In yet other examples, the generated responseis data content relevant to satisfying a user request in the speechinput.

Speech synthesis module 740 is configured to synthesize speech outputsfor presentation to the user. Speech synthesis module 740 synthesizesspeech outputs based on text provided by the digital assistant. Forexample, the generated dialogue response is in the form of a textstring. Speech synthesis module 740 converts the text string to anaudible speech output. Speech synthesis module 740 uses any appropriatespeech synthesis technique in order to generate speech outputs fromtext, including, but not limited, to concatenative synthesis, unitselection synthesis, diphone synthesis, domain-specific synthesis,formant synthesis, articulatory synthesis, hidden Markov model (HMM)based synthesis, and sinewave synthesis. In some examples, speechsynthesis module 740 is configured to synthesize individual words basedon phonemic strings corresponding to the words. For example, a phonemicstring is associated with a word in the generated dialogue response. Thephonemic string is stored in metadata associated with the word. Speechsynthesis model 740 is configured to directly process the phonemicstring in the metadata to synthesize the word in speech form.

In some examples, instead of (or in addition to) using speech synthesismodule 740, speech synthesis is performed on a remote device (e.g., theserver system 108), and the synthesized speech is sent to the userdevice for output to the user. For example, this can occur in someimplementations where outputs for a digital assistant are generated at aserver system. And because server systems generally have more processingpower or resources than a user device, it is possible to obtain higherquality speech outputs than would be practical with client-sidesynthesis.

Additional details on digital assistants can be found in the U.S.Utility application Ser. No. 12/987,982, entitled “Intelligent AutomatedAssistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No.13/251,088, entitled “Generating and Processing Task Items ThatRepresent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosuresof which are incorporated herein by reference.

4. Processes for Intelligent List Reading

FIGS. 8A-D illustrate process 800 for operating a digital assistant toperform intelligent list reading, according to various examples. FIGS.9A-D illustrate intelligent list reading performed by digital assistant905 implemented on user device 903 in response to spoken interactionfrom user 901, according to various examples. Process 800 is performed,for example, using one or more electronic devices implementing a digitalassistant. In some examples, the process is performed at a client-serversystem (e.g., system 100) implementing a digital assistant. In someexamples, the process is performed at a user device (e.g., device 104,200, 400, or 600). In process 800, some blocks are, optionally,combined, the order of some blocks are, optionally, changed, and someblocks are, optionally, omitted. Further, it should be recognized thatin some examples, only a subset of the features described below withreference to FIGS. 8A-D is performed in process 800.

At block 802, a spoken user request is received (e.g., at I/O processingmodule 728 and via microphone 213). The spoken user request isassociated with a user and is addressed to the digital assistantimplemented on the user device. Additionally, the spoken user request isassociated with a plurality of data items. For example, the spoken userrequest is a request to obtain and provide a list of restaurants(“What's good to eat?”) or a list of song titles (“Recommend me somemusic.”).

At block 804, a determination is made (e.g., by natural languageprocessing module 732) as to whether a degree of specificity of thespoken user request is less than a threshold level. The determination isbased on the domain corresponding to the spoken user request, the sizeof the metadata associated with data items that satisfy the spoken userrequest, the degree of familiarity associated with data items thatsatisfy the spoken user request, or the number of parameters defined inthe spoken user request. Each of these factors is discussed in greaterdetail below. Based on the determination, the requested information ispresented in a manner that optimizes user experience. In particular, aspoken user request having a degree of specificity that is less than athreshold level indicates a vague user request that corresponds to auser intent of wanting to explore content. In other words, an inferenceis made from the vague user request that the user likely does not haveany specific data items in mind, but rather wishes to explore availablecontent and obtain a recommendation. In contrast, a spoken user requesthaving a degree of specificity that is greater than a threshold levelindicates a more specific user request that corresponds to a user intentof wanting to obtain specific data items. The digital assistant thustailors the response according to the appropriate inferred user intent.

Block 804 includes determining a user intent (e.g., actionable intent)corresponding to the spoken user request. The user intent is determinedin a manner described above with respect to FIGS. 7A-C. In particular,as discussed above, determining the user intent includes determining adomain corresponding to the spoken user request. Whether the spoken userrequest has a degree of specificity less than a threshold value dependson the specific domain corresponding to the spoken user request. Inparticular, the threshold value is established based on the specificdomain corresponding to the spoken user request. For example, certaindomains, such as the “music,” “restaurant,” or “business search”domains, encompass subject matter that is more predisposed to contentexploration. The threshold value for these domains is thus higher wherethe digital assistant is more likely to infer from the spoken userrequest that the user wishes to explore available content and obtain arecommendation. Accordingly, in these examples, the degree ofspecificity of the spoken user request is more likely to be less thanthe threshold level. Other domains, such as the “sport schedules,”“movies playing,” “movie info,” “alarm list,” “spelling,” “rhyming,” or“dictionary definition” domains, encompass subject matter that is morespecific and better defined. The threshold value for these domains isthus lower where the digital assistant is more likely to infer from thespoken user request that the user wishes to obtain specific data items.Accordingly, in these examples, the degree of specificity of the spokenuser request is more likely not to be less than the threshold level.

Additionally, block 804 includes determining the size of the metadataassociated with data items that satisfy the spoken user request. Inparticular, the threshold value is established based on the determinedsize of the metadata associated with data items that satisfy the spokenuser request. The size of the metadata is based on the number ofparameters or attributes associated with the data items. For example,data items that satisfy the spoken user request “What's good to eat?”are associated with a large amount of metadata. Specifically, each dataitem representing a specific restaurant includes metadata definingvarious parameters, such as the name of the restaurant, cuisine, pricerange, phone number, location (e.g., address), user rating, hours ofoperation, whether reservations are accepted, and the like. Thethreshold value for these types of spoken user requests is thus higherwhere the digital assistant is more likely to infer from the spoken userrequest that the user wishes to explore available content and obtain arecommendation. Accordingly, in these examples, the degree ofspecificity of the spoken user request is more likely to be less thanthe threshold level. Conversely, data items that satisfy the spoken userrequest “What does ‘plot’ mean?” are associated with a small amount ofmetadata. Specifically, each data item representing a dictionarydefinition of the word “plot” includes metadata defining a limitednumber of parameters such as the source of the dictionary definition.The threshold value for these types of spoken user requests is thuslower where the digital assistant is more likely to infer from thespoken user request that the user wishes to obtain specific data items.Accordingly, in these examples, the degree of specificity of the spokenuser request is more likely not to be less than the threshold level.

Block 804 further includes determining a degree of familiarityassociated with data items that satisfy the spoken user request. Inparticular, the threshold value is established based on the determineddegree of familiarity associated with data items that satisfy the spokenuser request. The degree of familiarity represents how familiar the useris with the data items being requested. For example, data itemsassociated with the user are determined to have a higher degree offamiliarity. Such data items include, for example, music items from theuser's personal music library, contact information from the user'scontacts, or applications installed on the user device. Thus, for spokenuser requests such as “Tell me all contacts for ‘John,’” “Which Eaglessongs do I have?” or “Tell me the alarms I have set,” the thresholdvalue is lower where the digital assistant is more likely to infer thatthe user wishes to obtain specific data items. Accordingly, in theseexamples, the degree of specificity of the spoken user request is morelikely to be less than the threshold level. In other examples, thedegree of familiarity is based on how recent or how frequent the useraccesses or interacts with the requested data items. For example, if theuser frequently browses through many Katy Perry music albums or recentlylistened to many Taylor Swift songs, then the degree of familiarityassociated with these data items would be higher. Thus, for spoken userrequests such as “What are some Katy Perry albums?” or “List me someTaylor Swift songs,” the threshold value is lower where the digitalassistant is more likely to infer that the user wishes to obtainspecific data items. Accordingly, in these examples, the degree ofspecificity of the spoken user request is more likely not to be lessthan the threshold level.

In some examples, block 804 includes determining the number ofparameters defined in the spoken user request. In particular, the degreeof specificity is based on the determined number of parameters. Theparameters represent the properties in the determined domaincorresponding to spoken user request. A greater number of parametersdefined in the spoken user request are associated with a higher degreeof specificity, whereas a lower number of parameters defined in thespoken user request are associated with a lower degree of specificity.For example, the spoken user request “Recommend me some music”corresponds to the “music” domain. However, no specific parameters ofthe music domain are defined in the spoken user request. Based on theabsence of any parameters defined in the spoken user request, the degreeof specificity for this spoken user request is determined to be low.Accordingly, in this example, the degree of specificity of the spokenuser request is more likely to be less than the threshold level. Inanother example, the spoken user request is “Recommend me some good R&Bsongs by Beyonce.” In this example, the spoken user request definesseveral parameters associated with the music domain, including{genre}=R&B, {artist}=Beyonce, and {rating}=good. Based on having manydefined parameters, the degree of specificity for the spoken userrequest is determined to be higher. Accordingly, in this example, thedegree of specificity of the spoken user request is more likely not tobe less than the threshold level.

In some examples, block 804 includes determining the number of possibledata items that satisfy the spoken user request. In these examples, thedegree of specificity is based on the determined number of possible dataitems. In particular, a greater number of possible data items areassociated with a lower degree of specificity, whereas a smaller numberof possible data items are associated with a higher degree ofspecificity. For example, the spoken user request “What are some MichaelJackson songs?” is associated with a larger number of possible dataitems and thus have a lower degree of specificity. Accordingly, in thisexample, the degree of specificity of the spoken user request is morelikely to be less than the threshold level. In comparison, the spokenuser request “Tell me some Orianthi songs” is associated with a smallernumber of possible data items and thus have a higher degree ofspecificity. Accordingly, in this example, the degree of specificity ofthe spoken user request is more likely not to be less than the thresholdlevel.

In response to determining that a degree of specificity of the spokenuser request is less than a threshold level, one or more of blocks806-838 are performed. Specifically, in response to determining that adegree of specificity of the spoken user request is less than athreshold level, one or more of blocks 806-838 are performedautomatically without additional input from the user. In general, blocks806-838 involve helping the user explore a diverse range of content byrecommending data items in a more focused manner.

At block 806, one or more attributes (e.g., parameters) related to theuser request are determined (e.g., by natural language processing module732, task flow processing module 736, and/or service processing module738). In particular, the one or more attributes determined at block 806are not defined in the spoken user request of block 802. Rather, thedigital assistant intelligently determines the one or more attributes tohelp narrow down the user request and recommend the most relevant dataitems to the user.

In some examples, the one or more attributes are determined based on thefrequency with which an attribute is specified in a plurality ofprevious user requests from a plurality of users. For example, theprevious user requests of many users are analyzed to determine the mostfrequently requested attributes with respected to a given domain. Themost frequently requested attributes are then included in the one ormore attributes. For example, if it is determined that “Katy Perry” isfrequently specified in previous user requests related to the “music”domain, then the one or more attributes are determined to include theattribute “Katy Perry.”

In some examples, the one or more attributes are determined based on themost relevant attributes with respect to the given domain. For example,songs that are more recently released are more relevant than songs thathave been released many years back. Thus, for user requests related tothe “music” domain, the one or more attributes are determined to includea release date that is less than a predetermined number of years old. Inanother example, for user requests related to the “restaurant” domain,it is determined that restaurants located closer to the user's currentlocation can be more relevant that restaurants located much furtheraway. Thus, in this example, the one or more attributes are determinedto include a distance that is less than a predetermined number of milesfrom the user's current location.

In some examples, the one or more attributes are determined based on auser profile associated with the spoken user request. The user profileindicates various characteristics associated with the user and isgenerated based on user data (e.g., user's location, user music files,user emails/messages, etc.) and/or user input (e.g., user text input,user browsing history, user search history, etc.). The one or moreattributes of block 806 are thus based on the characteristics indicatedin the user profile. For example, based on the user profile indicating aKorean ethnicity, the one or more attributes include Korean cuisine forspoken user requests related to the “restaurant” domain. In anotherexample, based on the user profile indicating a tendency to browse popsongs, the one or more attributes include the “pop” genre for spokenuser requests related to the “music” domain.

In some examples, the one or more attributes are determined by randomlyselecting an attribute from a category of attributes. This is desirablefor helping the user to explore new or fresh data items that may piquethe user's interest. For example, based on the spoken user request“What's good to eat,” a random selection from a set of cuisines (e.g.,French, Chinese, Italian, French, Mexican, Seafood, New American, etc.)is made. The digital assistant thus provides (e.g., at block 812), forexample, a recommended Seafood restaurant in one instance of the spokenuser request and a recommended Mexican restaurant in another separateinstance of the same spoken user request.

At block 808, a list of data items is obtained based on the spoken userrequest and the one or more attributes. In particular, a structuredquery based on the determined user intent of the spoken user request isgenerated (e.g., by natural language processing module 732). Thestructured query includes the attributes defined in the spoken userrequest and the one or more attributes determined at block 806. A searchis then performed (e.g., by task flow processing module 736 and/orservice processing module 738) in accordance with the structured queryto obtain a list of data items that satisfy the spoken user request. Forexample, based on the one or more attributes determined at block 806 andone or more attributes defined in the spoken user request of block 802,one or more information sources are searched to obtain the list of dataitems.

At block 810, a spoken response is generated (e.g., by task flowprocessing module 736, dialogue processing module 734, and/or speechsynthesis module 740). The spoken response comprises a subset of thelist of data items. In some examples, the subset of the list of dataitems has at most a predetermined number of data items. For example, thesubset of the list of data items has no more than 1 or 2 data items.Restricting the number of data items presented to the user improves userexperience. In particular, it enables the most relevant data item(s) tobe recommended to the user and prevent the user from becomingoverwhelmed by too many options.

In some examples, generating the spoken response includes generating aspoken preamble that provides some context to the subset of the list ofdata items to be presented. In particular, the spoken preamble describesan attribute of the one or more attributes determined at block 806.Further, the generated spoken response includes a description thatspecifies one or more additional attributes for each data item of thesubset of the list of data items. In particular, each of the one or moreadditional attributes is not defined in the spoken user request of block802 and is different from any of the one or more attributes of block806. The description serves to provide additional details of each dataitem, which helps the user determine whether the recommended data itemis acceptable or not.

In some examples, the spoken response is initially generated as a textresponse (e.g., with dialogue flow processing module 734) at block 810and then converted to speech (e.g., with speech synthesis module 740) atblock 812.

At block 812, the spoken response of block 810 is provided (e.g., usingspeech synthesis module 740, I/O processing module 728, and/or speaker211). Providing the spoken response includes providing the spokenpreamble followed by the subset of the list of data items in spokenform. In some examples, the spoken preamble is provided prior toproviding the subset of the list of data items. Additionally, providingthe spoken response includes providing a spoken prompt, as discussed ingreater detail below. The spoken response is provided at the user devicein the form of synthesized speech. Alternatively, the spoken response isprovided in the form of audio data that is played by the user device.

Blocks 802 through 812 of process 800 are further described withreference to the examples illustrated in FIGS. 9A-B. In FIGS. 9A-B, userdevice 903 is similar or identical to devices 104, 200, 400, or 600 andincludes any of the components of digital assistant system 700. Digitalassistant 905 is implemented at least partially on user device 903 andat least partially on a server (e.g., DA server 106). With reference toFIG. 9A, user 901 provides spoken user request 902 “Hey Siri, what'sgood to eat?” to digital assistant 905 of user device 903. Digitalassistant 905 receives (block 802) spoken user request 902 anddetermines (block 804) whether a degree of specificity of spoken userrequest 902 is less than a threshold level. In this example, spoken userrequest 902 is vague since it does not define any specific parametersother than “good.” In addition, the request is broad since it isassociated with a large number of data items. As discussed above inblock 804, such characteristics correspond to a low degree ofspecificity of spoken user request 902. Furthermore, spoken user request902 corresponds to the “restaurant” domain. In particular, spoken userrequest 902 is a request for restaurant data items, where each data itemis associated with a significant amount of metadata. As discussed abovein block 804, such additional characteristics correspond to a highthreshold value associated with the degree of specificity. Based on thelow degree of specificity of spoken user request 902 and the highthreshold value associated with the degree of specificity, the degree ofspecificity of spoken user request 902 is determined (block 804) to beless than the threshold level. In response to this determination,digital assistant 905 determines (block 806) the attribute “Japanesecuisine” to refine the request. Notably, “Japanese cuisine” is not anattribute defined in spoken user request 902. A search for Japaneserestaurants is performed to obtain (block 808) a list of Japaneserestaurants. The search is based on the determined attribute “Japanesecuisine” and the “restaurant” domain corresponding to spoken userrequest 902. In some examples, based on additional relevant parameterssuch as the distance from the user's current location and popularity(e.g., user ratings), the search is further refined to obtain the listof Japanese restaurants. Spoken response 904 is then generated (block810) and provided (block 812) to the user. As shown, spoken response 904includes the Japanese restaurant “Gochi Japanese Fusion Tapas,” which isone (i.e., a subset) of the Japanese restaurants in the obtained list ofJapanese restaurants. “Gochi Japanese Fusion Tapas” is selected from thelist of Japanese restaurants based on factors such as popularity,relevance, distance for the user's current location, or price. Forexample, it is the most popular restaurant within half a mile of theuser's current location. As shown, spoken response 904 includes thepreamble “If you like Japanese food,” which indicates that the cuisineassociated with the recommended restaurant is “Japanese.” Additionally,spoken response 904 includes the additional description “moderatelypriced” and “four-star user rating” about the recommended restaurant“Gochi Japanese Fusion Tapas,” which is helpful to the user fordetermining whether or not the recommendation is acceptable.

Turning now to the example shown in FIG. 9B, user 901 provides spokenuser request 922 “Hey Siri, recommend me some music?” to digitalassistant 905 of user device 903. Digital assistant 905 receives (block802) spoken user request 922 and determines (block 804) whether a degreeof specificity of spoken user request 922 is less than a thresholdlevel. Similar to spoken user request 902 of FIG. 9A, spoken userrequest 922 is vague since it does not define any specific parametersrelated to “music.” Additionally, spoken user request 922 is broad sinceit is associated with a large number of data items. As discussed abovein block 804, such characteristics correspond to a low degree ofspecificity of spoken user request 922. Moreover, spoken user request922 corresponds to the “music” domain. In particular, spoken userrequest 922 is a request for music data items, where each data item isassociated with a significant amount of metadata. As discussed above inblock 804, such additional characteristics correspond to a highthreshold level associated with the degree of specificity. Based onthese factors, a threshold level associated with the degree ofspecificity is determined to be high. Based on the low degree ofspecificity of spoken user request 922 and the high threshold valueassociated with the degree of specificity, the degree of specificity ofspoken user request 922 is determined (block 804) to be less than thethreshold level. In response to this determination, digital assistant905 determines the attribute of genre=“alternative” music (block 806) torefine the request. Notably, the genre of “alternative” music is not anattribute defined in spoken user request 922. A search for alternativemusic is performed to obtain (block 808) a list of songs having thegenre of “alternative” music. Spoken response 924 is then generated(block 810) and provided (block 812) to the user. As shown, spokenresponse 924 includes the alternative song “Under the Blacklight,” whichis one (i.e., a subset) of the alternative songs in the obtained list ofalternative songs. “Under the Blacklight” is selected from the list ofalternative songs based on factors such as popularity, relevance, orrecent release date. Spoken response 924 also includes the preamble “Ifyou're in the mood for Alternative,” which indicates that the genreassociated with the recommended song is “alternative.” In particular,the preamble helps contextualize the data item “Under the Blacklight”that follows. Additionally, spoken response 924 includes the additionaldescription “Rilo Kiley” to indicate the artist associated with therecommended song “Under the Blacklight. Specifically, as opposed to onlynaming song title in spoken response 924, providing additionalinformation (e.g., the artist or the release date) about the recommendsong makes the recommendation more meaningful to the user.

In some examples, the spoken response of blocks 810 and 812 optionallyincludes a spoken prompt. In particular, the spoken prompt indicates tothe user that additional data items are available. For example, as shownin FIG. 9B, spoken response 924 includes the spoken prompt “Does thatwork?” Other examples of similar spoken prompts include “There's more,”“Does that sound good?” or “There are other options.” The user is thusprovided with the option to respond. For example, the microphone of theuser device is turned on to receive audio input while or after providingthe spoken response at block 812. The audio input is then analyzed todetermine whether it contains a follow-up spoken user request. If it isdetermined that the audio input contains a follow-up spoken userrequest, a suitable response is generated and provided, as described ingreater detail below.

In some examples, the spoken prompt is intelligently provided. Inparticular, the spoken prompt is initially provided for a predeterminednumber of times to a user to inform the user of the option to requestfor additional data items. The spoken prompt then ceases to be providedafter the predetermined number of times under the assumption that theuser already understands the option to request for additional dataitems. For example, block 810 includes determining whether each of apredetermined number of previous spoken responses includes a spokenprompt indicating that additional data items are available. In responseto determining that each of a predetermined number of previous spokenresponses does not include a spoken prompt indicating that additionaldata items are available, the spoken prompt is generated and provided inthe spoken response. Conversely, in response to determining that each ofa predetermined number of previous spoken responses includes a spokenprompt indicating that additional data items are available, process 800forgoes including the spoken prompt in the spoken response.

Turning now to block 814 of process 800 shown in FIG. 8B, speech inputis received from the user (e.g., at I/O processing module 728 and viamicrophone 213). In some examples, the microphone of the user device isautomatically (e.g., without further user input) turned on to receivespeech input while or after providing the spoken response at block 812.In some examples, the microphone is turned on at all times. The speechinput of block 814 is then received while the microphone is turned on.In other examples, the microphone of the user device is notautomatically turned on while or after providing the spoken response atblock 812. Instead, the microphone is turned on while or after thespoken response of block 812 is provided in response to receiving a userinput. The speech input is thus received after the microphone is turnedon in response to the user input. The speech input is responsive to thespoken response provided at block 812. For example, the speech inputindicates an acceptance or rejection of the recommendation provided inthe spoken response of block 812.

In some examples, the speech input is received while the spoken responseis provided at block 812. In particular, the user interrupts the digitalassistant during the spoken response and provides the spoken response.Upon detecting the speech input while the spoken response is beingprovided, the digital assistant ceases to provide the rest of the spokenresponse. Further, in response to receiving the speech input, one ormore of blocks 816 to 838 are performed.

At block 816, a determination is made as to whether the speech inputcorresponds to a rejection of the subset of the list of data items. Thedetermination is made using natural language processing (e.g., withnatural language processing module 732). For example, with reference toFIG. 9A, speech input 906 “Nah, something else” is received from theuser in response to spoken response 904. The words and phrases in speechinput 906 are parsed and analyzed to determine whether they correspondto a user intent of rejecting the subset of the list of data itemsprovided in spoken response 904. In particular, it is determine that thephrases “nah” and “something else” both correspond to a negativeresponse and based on this determination, speech input 906 is determinedto correspond to a rejection of the subset of the list of data items.Other examples of speech input corresponding to a rejection of thesubset of the list of data items include “No,” “Nope,” “Anything butthat,” “Don't like . . . ,” “Try again,” “Any others?” or the like. Inresponse to determining that the speech input of block 814 correspondsto a rejection of the subset of the list of data items, one or more ofblocks 818 to 834 are performed. Specifically, in response todetermining that the speech input of block 814 corresponds to arejection of the subset of the list of data items, one or more of blocks818 to 834 are performed automatically without additional input from theuser.

At block 818, one or more second attributes related to the spoken userrequest of block 802 are determined (e.g., by natural languageprocessing module 732, task flow processing module 736, and/or serviceprocessing module 738). The one or more second attributes are differentfrom the one or more attributes of block 806. Additionally, the one ormore second attributes may not be defined in the spoken user request ofblock 802. Block 818 is similar to block 806, except that differentattributes related to the spoken user request are determined. Forexample, referring back to FIG. 9A, the digital assistant determines oneor more second attributes related to restaurants to refine spoken userrequest 902 and provide a different recommendation to the user. In thisexample, the digital assistant determines the second attribute “Chinesecuisine,” which is different from the attribute “Japanese cuisine”determined at block 806. Moreover, “Chinese cuisine” is not defined inspoken user request 902.

At block 820, a second list of data items is obtained (e.g., usingnatural language processing module 732, task flow processing module 736,and/or service processing module 738) based on the user request and theone or more second attributes. Block 820 is similar to block 808, exceptthat the second list of data items is based on the one or more secondattributes of block 818 rather than the one or more attributes of block806. For example, based on the one or more second attributes determinedat block 818 and/or one or more attributes defined in the spoken userrequest of block 802, one or more information sources are searched toobtain the second list of data items. In the example shown in FIG. 9A, asearch for Chinese restaurants is performed to obtain a list of Chineserestaurants. The search is based on the second attribute “Chinesecuisine” and the “restaurant” domain corresponding to spoken userrequest 902. In some examples, based on additional relevant parameterssuch as the distance from the user's current location and popularity(e.g., user ratings), the search is further refined to obtain the listof Chinese restaurants.

At block 822, a second spoken response is generated (e.g., by task flowprocessing module 736, dialogue processing module 734, and/or speechsynthesis module 740). The second spoken response comprises a subset ofthe second list of data items. Block 822 is similar to block 810, exceptthat the second spoken response comprises a subset of the second list ofdata items rather than a subset of the first list of data items.

At block 824, the second spoken response of block 822 is provided (e.g.,using speech synthesis module 740, I/O processing module 728, and/orspeaker 211). Block 824 is similar to block 812.

For example, in FIG. 9A, second spoken response 908 is generated (block822) and provided (block 824) to the user in response to speech input906. As shown, second spoken response 908 includes the Chineserestaurant “Mandarin Gourmet,” which is one (i.e., a subset) of theChinese restaurants in the obtained list of Chinese restaurants (e.g.,second list of data items obtained at block 820). “Mandarin Gourmet” isselected from the list of Chinese restaurants based on factors such aspopularity, relevance, distance for the user's current location, orprice. Second spoken response 908 includes the additional description“Chinese restaurant,” “close by,” and “moderately priced” regarding therecommended restaurant “Mandarin Gourmet,” which is helpful to the userfor determining whether to accept the recommendation. Although in thisexample, the preamble does not indicate the one or more secondattributes of block 818, it should be recognized that in other examples,second spoken response 908 is generated at block 822 with a similarspoken preamble as spoken response 904 to help contextualize therecommended restaurant that follows. For example, second spoken response908 could instead be “How about Chinese cuisine then? There's MandarinGourmet close by that is moderately priced.”

It should be appreciated that, in some examples, blocks 814 through 824of process 800 are repeated multiple times to help the user explore theavailable content. For example, through one or more cycles of blocks 814through 824, the user provides several speech inputs (block 814)rejecting the various recommendations of the digital assistant and eachtime, the digital assistant responds by provide a differentrecommendation (blocks 820-824) by determining different attributes(block 818) related to the original spoken request. In this way, thedigital assistant helps the user explore a large diverse body of content(e.g., restaurant).

In some examples, the digital assistant requests guidance from the userto assist with providing a suitable recommendation. For example,referring back to block 816, in response to determining that the speechinput of block 814 corresponds to a rejection of the subset of the listof data items, one or more of blocks 826 to 834 are performed. Blocks826 to 834 are directed to obtaining guidance from the user andproviding a recommendation to the user based on the obtained guidance.

At block 826, a spoken prompt for the user to provide additionalattributes to refine the spoken user request is provided. The spokenprompt serves as a request for guidance from the user to help thedigital assistant provide recommendations that better match the user'scurrent preferences. In some examples, the spoken prompt is provided inresponse to receiving a predetermined number of speech inputs rejectingthe recommendations of the digital assistant. For example, in FIG. 9A,speech input 910 “No . . . ” is received in response to providing secondspoken response 908. In this example, the digital assistant determinesthat two speech inputs (e.g., 906 and 910) have been received rejectingthe digital assistant's restaurant recommendations (e.g., 904 and 908)and in response to the determination, the digital assistant providesspoken prompt 912 “OK. What cuisine are you in the mood for?”Specifically, spoken prompt 912 asks the user to provide a preferredcuisine attribute to help the digital assistant refine spoken userrequest 902.

At block 828, a second speech input is received (e.g., at I/O processingmodule 728 and via microphone 213). The second speech input isresponsive to the spoken prompt of block 826. For example, the secondspeech input defines one or more attributes related to the spoken userrequest of block 902. As shown in FIG. 9A, second speech input 914 “Howabout Mexican?” is received. In this example, second speech input 914defines the attribute “Mexican” cuisine, which helps digital assistant905 refine spoken user request 902.

In some examples, a determination is made (e.g., using natural languageprocessing module 732) as to whether the second speech input defines oneor more attributes related to the spoken user request. In response todetermining that the second speech input defines one or more attributesrelated to the spoken user request, block 830 is performed. Conversely,in response to determining that the second speech input does not defineone or more attributes related to the spoken user request, the digitalassistant either provides the spoken prompt of block 826 again orprovides a spoken response indicating an error.

At block 830, a third list of data items is obtained (e.g., usingnatural language processing module 732, task flow processing module 736,and/or service processing module 738) based on the user request and oneor more attributes defined in the second speech input. Block 830 issimilar to block 808, except that the third list of data items is basedon the one or more attributes defined in the second speech input ofblock 828 rather than the one or more attributes of block 806. Forexample, based on the one or more attributes defined in the secondspeech input of block 828 and/or one or more attributes defined in thespoken user request of block 802, one or more information sources aresearched to obtain the third list of data items. In the example shown inFIG. 9A, a search for Mexican restaurants is performed to obtain a listof Mexican restaurants. The search is based on the attribute “Mexican”cuisine defined in second speech input 914 and the “restaurant” domaincorresponding to spoken user request 902. In some examples, based onadditional relevant parameters such as the distance from the user'scurrent location and popularity (e.g., user ratings), the search isfurther refined to obtain the list of Mexican restaurants.

At block 832, a third spoken response is generated (e.g., by task flowprocessing module 736, dialogue processing module 734, and/or speechsynthesis module 740). The third spoken response comprises a subset ofthe third list of data items. Block 832 is similar to block 810, exceptthat the third spoken response comprises a subset of the third list ofdata items rather than a subset of the list of data items.

At block 834, the third spoken response is provided (e.g., using speechsynthesis module 740, I/O processing module 728, and/or speaker 211).Block 834 is similar to block 812.

Referring back to the example of FIG. 9A, third spoken response 916 isgenerated (block 832) and provided (block 834) to the user. As shown,third spoken response 916 includes the Mexican restaurant “AquiCal-Mex,” which is one (i.e., a subset) of the Mexican restaurants inthe obtained list of Mexican restaurants (e.g., third list of data itemsobtained at block 830). “Aqui Cal-Mex” is selected from the list ofMexican restaurants based on factors such as popularity, relevance,distance from the user's current location, or price. Third spokenresponse 916 includes the additional description “rated four stars,”“close by,” and “well known for their Industrial Strength Margaritas”regarding the recommended restaurant, which is helpful to the user fordetermining whether to accept the recommendation.

With reference back to block 816, in response to determining that thespeech input of block 814 does not correspond to a rejection of thesubset of the list of data items, one or more of blocks 836 or 838 areperformed. At block 836 (FIG. 8C), a determination is made as to whetherthe speech input corresponds to an acceptance of a data item in thesubset of the list of data items. The determination is made usingnatural language processing (e.g., with natural language processingmodule 732). For example, the words and phrases in the speech input areparsed and analyzed to determine whether they correspond to a userintent of accepting a data item in the subset of the list of data itemsprovided in the spoken response. In a specific example, it is determinethat the phrases “Yes” or “Cool” correspond to a positive response andbased on this determination, the speech input is determined tocorrespond to an acceptance of a data item in the subset of the list ofdata items. Other examples of speech input corresponding to anacceptance of the subset of the list of data items include “Soundsgood,” “Yeah,” “Good,” “Sure,” “Why not,” “Let's do it,” or the like.

In response to determining that the speech input of block 814 does notcorrespond to an acceptance of a data item in the subset of the list ofdata items, process 800 determines that the speech input of block 814corresponds to a new spoken user request and thus process 800 returns toblock 804 of FIG. 8A. Alternatively, in response to determining that thespeech input corresponds to an acceptance of a data item in the subsetof the list of data items, block 838 is performed. Specifically, inresponse to determining that the speech input corresponds to anacceptance of a data item in the subset of the list of data items, block838 is performed automatically without additional input from the user.At block 838, content associated with the accepted data item is provided(e.g., using task flow processing module 736 and service processingmodule 738).

Blocks 836 and 838 are further described with reference to the examplesillustrated in FIGS. 9A and 9B. For example, in FIG. 9A, speech input918 “That sounds good” is received from the user in response to thirdspoken response 916 recommending the “Aqui Cal-Mex” Mexican restaurant.In this example, it is determined that the phrase “sounds good”corresponds to a positive response and based on this determination,speech input 918 is determined (block 836) to correspond to anacceptance of the recommended “Aqui Cal-Mex” Mexican restaurant. Inresponse to this determination, the digital assistant provides contentassociated with the “Aqui Cal-Mex” Mexican restaurant. For example, asindicated in spoken response 920 of FIG. 9A, digital assistant 905retrieves the address associated with the “Aqui Cal-Mex” Mexicanrestaurant and display, on user device 903, directions to the restaurantfrom the user's current location. Alternatively, in another example,digital assistant 905 retrieves a webpage that provides additionalinformation (e.g., user reviews, menu, photos, etc.) related to the“Aqui Cal-Mex” Mexican restaurant and display the webpage on user device903.

In another example illustrated in FIG. 9B, user 901 provides speechinput 926 “Sure,” in response to spoken response 924 recommending thealternative song “Under the Blacklight” by Rilo Kiley. In this example,it is determined that the phrase “Sure” corresponds to a positiveresponse and based on this determination, speech input 926 is determined(block 836) to correspond to an acceptance of the recommended song“Under the Blacklight.” In response to this determination, digitalassistant 905 provides content associated with the alternative song“Under the Blacklight.” For example, as indicated by spoken response 928and musical notes 959 depicted in FIG. 9B, audio data corresponding tosong “Under the Blacklight” is retrieved by digital assistant 905 andthe audio data is played on user device 903. Alternatively, in anotherexample, information related to the alternative song “Under theBlacklight” is retrieved by digital assistant 905. For example, awebpage that provides critic reviews or user reviews of the song “Underthe Blacklight” and the band “Rilo Kiley” is retrieved and displayed onuser device 903.

With reference back to block 804, in response to determining that adegree of specificity of the spoken user request is not less than athreshold level, one or more of blocks 840-850 are performed. Blocks840-850 are directed to retrieving a specific list of requested dataitems and reading the list to the user in a manner than is easilycomprehended and retained by the user.

At block 840, a fourth list of data items is obtained (e.g., usingnatural language processing module 732, task flow processing module 736,and/or service processing module 738) based on the spoken user request.In particular, a structured query based on the determined user intent ofthe spoken user request is generated (e.g., by natural languageprocessing module 732). The structured query includes the attributesdefined in the spoken user request. Unlike in block 806, because thespoken use request is determined to be sufficiently specific (at block804), additional attributes are not determined to further refine thespoken user request. A search is then performed (e.g., by task flowprocessing module 736 and/or service processing module 738) inaccordance with the structured query to obtain the fourth list of dataitems that satisfy the spoken user request. For example, based on theone or more attributes defined in the spoken user request of block 802,one or more information sources are searched to obtain the fourth listof data items.

At block 842, a determination is made as to whether the number of dataitems in the fourth list of data items exceeds a predetermined number.The predetermined number is the maximum number of data items that theuser is inferred to be able to easily comprehend and retain via voiceinteraction. User experience is thus negatively impacted if more thanthe predetermined number of data items are provided to the user in thespoken response (e.g., at blocks 846 or 850).

The predetermined number is based on a number of factors. For example,the predetermined number is based on a degree of familiarity of the userwith data items in the fourth list of data items. If the user is morefamiliar with the data items in the fourth list of data items (higherdegree of familiarity), the predetermined number is greater where alarger number of data items is provided to the user in the spokenresponse. This is because the user is able to comprehend and retain alarger number of data items if the user is already familiar with thedata items. Conversely, if the user is less familiar with the data itemsin the fourth list of data items (lower degree of familiarity), thepredetermined number is lower where a smaller number of data items areprovided to the user in the spoken response.

In some examples, the degree of familiarity is determined based onwhether the data items in the fourth list of data items are associatedwith the user. For example, data items obtained from the user device(e.g., contact information obtained from contacts module 237) aredetermined to have a high degree of familiarity. Similarly, data itemsobtained from a library associated with the user (e.g., the user'spersonal media library) are determined to have a high degree offamiliarity. In other examples, the degree of familiarity is determinedbased on the frequency at which the data items in the fourth list ofdata items have been previously requested or reviewed by the user. Forexample, it is determined from usage logs that the user frequentlysearches and listens to Katy Perry songs. Thus, if the fourth list ofdata items contains a list of Katy Perry songs, the degree offamiliarity of the user with data items in the fourth list of data itemsis determined to be high.

In some examples, the predetermined number is based on the amount ofmetadata associated with each data item in the fourth list of dataitems. In particular, the predetermined number is determined to begreater if the amount of metadata associated with each data item in thefourth list of data items is small. A smaller amount of metadatacorresponds to less information that is to be provided with each dataitem and thus more data items are provided to the user withoutoverwhelming the user. Conversely, the predetermined number isdetermined to be lower if the amount of metadata associated with eachdata item in the fourth list of data items is large.

In some examples, the predetermined number is based on a cognitive loadassociated with the user at the time the spoken user request isreceived. The cognitive load refers to the total amount of mental effortbeing used in the working memory of the user. In some examples, thecognitive load associated with the user is inferred based on the numberof activities and/or the types of activities the user engages in. Forexample, a greater number of activities or mentally challenging types ofactivities is associated with a higher cognitive load. The number ofactivities and the types of activities that the user is engaged in aredetermined based on context information received by the user device andcontext information generated by or stored on the user device. Forexample, based on data generated by the accelerometers and GPS sensor ofthe user device, the user device detects that the user is acceleratingand traveling in a manner consistent with being in a moving car.Further, the user device detects a Bluetooth pairing between the userdevice and a device associated with the car. Based on this contextinformation, the user device determines that the user is driving a carand infers a corresponding amount of cognitive load associated with theuser. In another example, the user device determines that the user iswatching a video based on detecting a corresponding operating statusassociated with the media application of the user device. Based on thiscontext information, the user device infers a corresponding amount ofcognitive load associated with the user. The predetermined number isdetermined to be higher if the cognitive load associated with the useris small. A smaller cognitive load indicates that the user has greatermental capacity to process information and thus more data items areprovided to the user without overwhelming the user. Conversely, thepredetermined number is determined to be lower if the cognitive loadassociated with the user is large.

In response to determining that the number of data items in the fourthlist of data items exceeds a predetermined number, one or more of blocks844-846 are performed. Blocks 844-846 are directed to generating andproviding a response containing only a subset of the fourth list of dataitems. Providing only a subset rather than the entire fourth list ofdata items is desirable to prevent overwhelming the user with too muchinformation and to enhance the amount of information retained by theuser.

At block 844, a fourth spoken response is generated (e.g., by task flowprocessing module 736, dialogue processing module 734, and/or speechsynthesis module 740). The fourth spoken response comprises a subset ofthe fourth list of data items. Block 844 is similar to block 810,described above. In some examples, the number of data items in thesubset of the fourth list of data items is less than or equal to thepredetermined number. Restricting the number of data items presented inthe fourth spoken response based on the predetermined number improvesuser experience. In particular, it enables the most relevant dataitem(s) to be presented to the user first and prevent the user frombecoming overwhelmed by too much information at one time.

In some examples, block 844 includes selecting the subset of the fourthlist of data items from the fourth list of data items based on anattribute defined in the spoken user request. For example, the attributedefined in the spoken user request imposes an order on the fourth listof data items. The subset of the fourth list of data items is thenselected based on the imposed order. Specifically, in one example, thespoken user request is “What are the most popular movies playingnearby?” In this example, the spoken user request defines the attribute“popular.” Based on this attribute, the digital assistant retrieves alist of movies playing near the user's current location and rank thelist of movies based on popularity (e.g., box office results, criticreviews, user ratings, etc.) The N highest ranked movies with respect topopularity in the list of movies is then selected as the subset of thelist of movies (where N is an integer less than or equal to thepredetermined number).

In other examples, the fourth list of data items has an inherent orderand the subset of the fourth list of data items is selected from thesubset of the fourth list of data items based on the inherent order. Forexample, as discussed below with reference to FIG. 9D, definitionsretrieved from a dictionary reference have an inherent order. In theseexamples, based on the inherent order, the subset of the fourth list ofdata items includes the first N data items of the fourth list of dataitems, where N is an integer less than or equal to the predeterminednumber.

In some examples, generating the fourth spoken response includesgenerating a fourth spoken preamble that indicates a number of dataitems in the fourth list of data items. For example, in response to thespoken user request “What action movies are playing today?” the fourthspoken response includes the fourth spoken preamble “There are 5 actionmovies playing today . . . ” The fourth spoken preamble is desirable toprovide context for the amount of information that will follow and helpsthe user better capture and retain the information.

In some examples, the subset of the fourth list of data items iscontrary to an expected result inferred from the spoken user request. Inthese examples, the fourth spoken preamble indicates that the subset ofthe fourth list of data items is contrary to an expected result inferredfrom the spoken user request. For example, the spoken user request is“What's the closest gas station?” In this example, an inference is madebased on the word “closest” that the user is expecting only one result.However, the digital assistant determines that there are three gasstations that are approximately equidistant from the user's currentlocation. In this example, the fourth spoken preamble states “Well,there are 3 gas stations nearby . . . ” Particularly, the interjection“Well” indicates that the subset of the fourth list of data items iscontrary to the user's expected result. The subtle interjection providescontext for the information that will follow. Additionally, it enablesthe response to be more natural and personable, thereby improving userexperience.

In some examples, generating the fourth spoken response includesgenerating a fourth spoken prompt indicating that additional data itemsare available. The fourth spoken prompt is, for example, after thesubset of the fourth list of data items. For example, after the subsetof the fourth list of data items, the fourth spoken prompt includes thephrase “Let me know if you′d like to hear the rest” (e.g., spokenresponse 936 in FIG. 9D) to indicate that additional data items areavailable and to prompt the user to request for the additional dataitems. Further, in some examples, the fourth spoken prompt indicates thenumber of remaining data items in the fourth list of data items. Forexample, after the subset of the fourth list of data items, the fourthspoken prompt includes the phrase “There are 4 more” (spoken response932 in FIG. 9C) to indicate that there are four remaining data items inthe fourth list of data items that have yet to be presented. The fourthspoken prompt thus provides the user the option to accept the data itemsthat have already been presented or to request that the remaining dataitems be presented.

As discussed above, the fourth spoken prompt is intelligently provided.For example, a determination is made as to whether each of apredetermined number of previous spoken responses includes a spokenprompt indicating that additional data items are available. In responseto determining that each of a predetermined number of previous spokenresponses does not include a spoken prompt indicating that additionaldata items are available, the fourth spoken prompt is generated toindicate that additional data items are available. Conversely, inresponse to determining that each of a predetermined number of previousspoken responses includes a spoken prompt indicating that additionaldata items are available, fourth spoken prompt may not be generated toindicate that additional data items are available.

At block 846, the fourth spoken response is provided (e.g., using speechsynthesis module 740, I/O processing module 728, and/or speaker 211).Block 846 is similar to block 812.

Referring back to block 842, in response to determining that the numberof data items in the fourth list of data items does not exceed apredetermined number, one or more of blocks 848-850 are performed.Blocks 848-850 are directed to generating and providing a response thatcontains the entire fourth list of data items.

At block 848, a fifth spoken response is generated (e.g., by task flowprocessing module 736, dialogue processing module 734, and/or speechsynthesis module 740). In particular, the fifth spoken responsecomprises the fourth list of data items. Block 848 is similar to block844, described above. Notably, because the number of data items in thefourth list of data items does not exceed the predetermined number, theentire fourth list of data items is included in the fifth spokenresponse without overwhelming the user. The fifth spoken responseincludes a fifth spoken preamble and/or a fifth spoken prompt that issimilar to the fourth spoken preamble and the fourth spoken promptdescribed above in block 844.

At block 850, the fifth spoken response is provided (e.g., using speechsynthesis module 740, I/O processing module 728, and/or speaker 211).Block 850 is similar to block 846.

Blocks 840 through 850 of process 800 are further described withreference to the examples illustrated in FIGS. 9C-D. As shown in FIG.9C, user 901 provides spoken user request 930 “Hey Siri, what does‘plot’ mean?” to digital assistant 905 of user device 903. In thisexample, digital assistant 905 determines that spoken user request 930is a specific request associated with the “dictionary” domain. Thus, thedegree of specificity of spoken user request 930 is determined to be notless than a threshold level (block 804). In response to thisdetermination, a list of six definitions for the word “plot” areretrieved (block 840) from a dictionary reference. Because user 901 islikely not familiar with the definitions and because the informationdensity for each definition is relatively high, the predetermined numberare determined (block 842) to be relatively low. For example, it aredetermined that at most two definitions (i.e., predetermined number=2)should be presented to user 901 at a given time to avoid overwhelminguser 901. Digital assistant 905 then determines (block 842) that thenumber of obtained definitions (e.g., six) exceeds the predeterminednumber (e.g., two) and thus only a subset (e.g., two definitions) of thelist of definitions is selected (block 844). In this example, theobtained list of definitions has an inherent order according to thedictionary reference. Thus, the first two definitions of the dictionaryreference are selected as the subset of the list of definitions. Asshown in FIG. 9C, spoken response 932 containing the two selecteddefinitions is generated (block 844) and provided (block 846) to user901. In this example, spoken response 932 includes the spoken preamble“Plot has multiple meanings . . . ,” which provides context to user 901that there is more than one definition of the word “plot” found.Additionally, spoken response 932 includes ordinal numbers (e.g., “First. . . ,” “Second . . . ”) that preface each of the two definitions.Further, spoken response 932 includes the spoken prompt “There are 4more, if you′d like to hear them.” The spoken prompt informs user 901that there are additional definitions available and the exact number ofadditional definitions. The spoken prompt also prompts user 901 torequest for the additional definitions, if desired. It should berecognized that if the list of definitions only had one or twodefinitions (rather than six), then the entire list of one or twodefinitions would be included in spoken response 932 (blocks 848-850).In such an example, no spoken prompt indicating that additionaldefinitions are available would be included.

In another example shown in FIG. 9D, user 901 provides spoken userrequest 934 “Hey Siri, any good movies playing today?” In this example,digital assistant 905 determines that there are a limited number ofmovies playing that given day near the user's current location. Thus,the degree of specificity of spoken user request 930 is determined to benot less than a threshold level (block 804). In response to thisdetermination, for example, a list of over twenty movies currentlyplaying near the user's location is retrieved (block 840) from a moviedatabase. In this example, the movie titles in the list of movies arerelatively short and thus the predetermined number is determined (block842) to be a moderate number (e.g., five). For example, it is determinedthat up to five movie titles can be presented to user 901 at a giventime without overwhelming user 901. Digital assistant 905 thendetermines (block 842) that the number of movies (e.g., greater thantwenty) in the list of retrieved movies exceeds the predetermined number(e.g., five) and thus only a subset (e.g., five movies) of the list ofmovies is selected (block 844). In this example, the list of movies doesnot have an inherent order. However, spoken user request 934 defines theattribute “good” and thus the list of twenty movies is ranked (block844) according to popularity and/or reviews and the five most popularmovies in the list are selected as the subset of the list of movies. Asshown in FIG. 9D, spoken response 936 containing the selected subset ofthe list of movies is generated (block 844) and provided (block 846) touser 901. In this example, spoken response 936 includes the spokenpreamble “I found a lot of movies playing today . . . ,” which providescontext to user 901 that a large number of movies are playing and only aportion of these movies is provided in spoken response 936. Further,spoken response 932 includes the spoken prompt “Let me know if you′dlike to hear the rest,” which prompts user 901 to request for theadditional movies in the list, if desired.

In some examples, process 800 allows for the user to provide follow-upspoken requests while or after the spoken response is provided. Forexample, while or after providing the spoken response (e.g., the first,second, third, fourth, or fifth spoken responses of blocks 812, 824,834, 846, or 850, respectively), the user device receives audio input(e.g., by turning on microphone 213). The audio input is then analyzedto determine whether it contains a follow-up spoken request. Thistherefore enables the user to request additional data items not providedin the spoken response (e.g., in response to the spoken prompt).Alternatively, the user provides a new spoken user request. In someexamples, if no follow-up spoken request is detected in the audio input,the user device ceases to receive audio input after a predeterminedamount of time. Thus, the user is not forced into another interaction ifno follow-up request is desired.

In one example, while providing a spoken response (e.g., the first,second, third, fourth, or fifth spoken responses of blocks 812, 824,834, 846, or 850, respectively), a second user request is detected. Forexample, in FIG. 9D, while digital assistant 905 is providing spokenresponse 936, user 901 interrupts and provides a second user request. Inresponse to detecting the second user request, digital assistant 905ceases to provide the remaining portion of spoken response 936. Forexample, if the second user request were detected by digital assistant905 while or after the movie title “The Revenant” is provided, butbefore the movie title “The Big Short” is provided, digital assistant905 ceases to provide the remaining portion of spoken response 936 afterthe movie title “The Revenant.” Digital assistant 905 then generates aspoken response based on the second user request and a portion of thefourth spoken response that coincides with detecting the second userrequest. For example, while digital assistant 905 is providing the movietitle “The Revenant,” user 901 interrupts with the second user request“What's that about?” Digital assistant 905 determines that the detectionof the second spoken request coincided with the portion “The Revenant”of spoken response 936. In response, digital assistant storesinformation related to the movie title “The Revenant” as context toprocess the second user request. In particular, digital assistant 905searches for plot information based on the movie title “The Revenant”and provides the plot information as a response to the second userrequest.

5. Other Electronic Devices

FIG. 10 shows a functional block diagram of electronic device 1000configured in accordance with the principles of the various describedexamples. The functional blocks of the device are optionally implementedby hardware, software, or a combination of hardware and software tocarry out the principles of the various described examples. It isunderstood by persons of skill in the art that the functional blocksdescribed in FIG. 10 are optionally combined or separated intosub-blocks to implement the principles of the various describedexamples. Therefore, the description herein optionally supports anypossible combination, separation, or further definition of thefunctional blocks described herein.

As shown in FIG. 10, electronic device 1000 includes touch screendisplay unit 1002 configured to display a graphical user interface andto receive touch input from the user, audio input unit 1004 configuredto receive audio input (e.g., speech input), speaker unit 1005configured to output audio (e.g., speech), and communication unit 1006configured to transmit and receive information. Electronic device 1000further includes processing unit 1008 coupled to touch screen displayunit 1002, audio input unit 1004, and communication unit 1006. In someexamples, processing unit 1008 includes receiving unit 1010, determiningunit 1012, obtaining unit 1014, generating unit 1016, providing unit1018, selecting unit 1020, detecting unit 1022, and ceasing unit 1024.

In accordance with some embodiments, processing unit 1008 is configuredto receive (e.g., with receiving unit 1010 and via audio input unit1004) a spoken user request (e.g., spoken user request of block 802)associated with a plurality of data items. Processing unit 1008 isfurther configured to determine (e.g., with determining unit 1012)whether a degree of specificity of the spoken user request (e.g., degreeof specificity of block 804) is less than a threshold level. Processingunit 1008 is further configured to, in response to determining that adegree of specificity of the spoken user request is less than athreshold level, determine (e.g., with determining unit 1012) one ormore attributes (e.g., one or more attributes of block 806) related tothe spoken user request, the one or more attributes not defined in thespoken user request. Processing unit 1008 is further configured toobtain (e.g., with obtaining unit 1014) a list of data items (e.g., listof data items of block 808) based on the spoken user request and the oneor more attributes. Processing unit 1008 is further configured togenerate (e.g., with generating unit 1016) a spoken response (e.g.,spoken response of block 810) comprising a subset of the list of dataitems. Processing unit 1008 is further configured to provide (e.g., withproviding unit 1018 and using speaker unit 1005) the spoken response(e.g., spoken response provided at block 812).

In some examples, processing unit 1008 is further configured todetermine (e.g., with determining unit 1012) a number of parameters(e.g., number of parameters of block 804) defined in the spoken userrequest. The degree of specificity is based on the number of parametersdefined in the spoken user request.

In some examples, processing unit 1008 is further configured todetermine (e.g., with determining unit 1012) a number of possible dataitems (e.g., number of possible data items of block 804) that satisfythe spoken user request. The degree of specificity is based on thenumber of possible data items.

In some examples, processing unit 1008 is further configured todetermine (e.g., with determining unit 1012) a size of metadata (e.g.,size of metadata of block 804) associated with data items that satisfythe spoken user request. The threshold level is based on the size of themetadata.

In some examples, processing unit 1008 is further configured todetermine (e.g., with determining unit 1012) a domain (e.g., domain ofblock 804) corresponding to the spoken user request. The threshold levelis based on the domain.

In some examples, processing unit 1008 is further configured todetermine (e.g., with determining unit 1012) a degree of familiarity(e.g., degree of familiarity of block 804) associated with data itemsthat satisfy the spoken user request. The threshold level is based onthe degree of familiarity.

In some examples, generating a spoken response includes generating aspoken preamble (e.g., spoken preamble of block 810) that describes anattribute of the one or more attributes. Further, providing the spokenresponse includes providing the spoken preamble prior to providing thesubset of the list of data items.

In some examples, the spoken response includes a description (e.g.,description of block 810) that specifies an additional attribute foreach data item of the subset of the list of data items. The additionalattribute is not defined in the spoken user request and is differentfrom any of the one or more attributes.

In some examples, the subset of the list of data items has at most apredetermined number of data items.

In some examples, processing unit 1008 is further configured to receive(e.g., with receiving unit 1010 and via audio input unit 1004) speechinput (e.g., speech input of block 814). Processing unit 1008 is furtherconfigured to, in response to receiving the speech input, determine(e.g., with determining unit 1012) whether the speech input correspondsto a rejection (e.g., rejection of block 816) of the subset of the listof data items. Processing unit 1008 is further configured to, inresponse to determining that the speech input corresponds to a rejectionof the subset of the list of data items, determine (e.g., withdetermining unit 1012) one or more second attributes (e.g., one or moresecond attributes of block 818) related to the spoken user request. Theone or more second attributes are different from the one or moreattributes and are not defined in the spoken user request. Processingunit 1008 is further configured to obtain (e.g., with obtaining unit1014) a second list of data items (e.g., second list of data items ofblock 820) based on the spoken user request and the one or more secondattributes. Processing unit 1008 is further configured to generate(e.g., with generating unit 1016) a second spoken response (e.g., secondspoken response of block 822) comprising a subset of the second list ofdata items. Processing unit 1008 is further configured to provide (e.g.,with providing unit 1018 and speaker unit 1005) the second spokenresponse (e.g., second spoken response provided at block 824).

In some examples, processing unit 1008 is further configured to, inresponse to determining that the speech input corresponds to a rejectionof the subset of the list of data items, provide (e.g., with providingunit 1018 and speaker unit 1005) a spoken prompt (e.g., spoken prompt ofblock 826) for the user to provide additional attributes to refine thespoken user request.

In some examples, processing unit 1008 is further configured to receive(e.g., with receiving unit and via audio input unit 1004) a secondspeech input (e.g., second speech input of block 828) responsive to thespoken prompt. Processing unit 1008 is further configured to obtain(e.g., with obtaining unit 1014) a third list of data items (e.g., thirdlist of data items of block 830) based on the spoken user request andone or more attributes defined in the second speech input. Processingunit 1008 is further configured to generate (e.g., with generating unit1016) a third spoken response (e.g., third spoken response of block 832)comprising a subset of the third list of data items. Processing unit1008 is further configured to provide (e.g., with providing unit 1018and speaker unit 1005) the third spoken response (e.g., third spokenresponse provided at block 834).

In some examples, processing unit 1008 is further configured to, inresponse to determining that the speech input does not correspond to arejection of the subset of the list of data items, determine (e.g., withdetermining unit 1012) whether the speech input corresponds to anacceptance (e.g., acceptance of block 836) of a data item in the subsetof the list of data items. Processing unit 1008 is further configuredto, in response to determining that the speech input corresponds to anacceptance of a data item in the subset of the list of data items,provide (e.g., with providing unit 1018 and speaker unit 1005) content(e.g., content of block 838) associated with the accepted data item.

In some examples, processing unit 1008 is further configured todetermine (e.g., with determining unit 1012) whether each of apredetermined number of previous spoken responses include a spokenprompt indicating that additional data items are available (e.g., blocks810 and 812). Processing unit 1008 is further configured to, in responseto determining that each of a predetermined number of previous spokenresponses do not include a spoken prompt indicating that additional dataitems are available, provide (e.g., with providing unit 1018 and speakerunit 1005), in the spoken response, a spoken prompt indicating thatadditional data items are available (e.g., block 810 and 812).Processing unit 1008 is further configured to, in response todetermining that each of a predetermined number of previous spokenresponses include a spoken prompt indicating that additional data itemsare available, forgo providing (e.g., with providing unit 1018), in thespoken response, a spoken prompt indicating that additional data itemsare available (e.g., block 810 and 812).

In some examples, processing unit 1008 is further configured to, inresponse to determining that a degree of specificity of the spoken userrequest is not less than a threshold level, obtain (e.g., with obtainingunit 1014) a fourth list of data items (e.g., fourth list of data itemsof block 840) based on the spoken user request. Processing unit 1008 isfurther configured to determine (e.g., with determining unit 1012)whether a number of data items in the fourth list of data items exceedsa predetermined number (e.g., block 842). Processing unit 1008 isfurther configured to, in response to determining that a number of dataitems in the fourth list of data items exceeds a predetermined number,generate (e.g., with generating unit 1016) a fourth spoken response(e.g., fourth spoken response of block 844) comprising a subset of thefourth list of data items. Processing unit 1008 is further configured toprovide (e.g., with providing unit 1018 and speaker unit 1005) thefourth spoken response (e.g., fourth spoken response provided at block846).

In some examples, generating the fourth spoken response includesgenerating a fourth spoken preamble (e.g., fourth spoken preamble ofblock 844) that indicates a number of data items in the fourth list ofdata items.

In some examples, the subset of the fourth list of data items iscontrary to an expected result inferred from the spoken user request,and wherein the fourth spoken preamble indicates that the subset of thefourth list of data items is contrary to an expected result inferredfrom the spoken user request.

In some examples, a number of data items in the subset of the fourthlist of data items is less than or equal to the predetermined number.

In some examples, processing unit 1008 is further configured to, inresponse to determining that a number of data items in the fourth listof data items does not exceed a predetermined number, generate (e.g.,with generating unit 1016) a fifth spoken response (e.g., fifth spokenresponse of block 848) comprising the fourth list of data items.Processing unit 1008 is further configured to provide (e.g., withproviding unit 1018 and speaker unit 1005) the fifth spoken response(e.g., fifth spoken response provided at block 850).

In some examples, processing unit 1008 is further configured to select(e.g., with selecting unit 1020), based on an attribute defined in thespoken user request, the subset of the fourth list of data items fromthe fourth list of data items (e.g., block 844).

In some examples, the fourth list of data items has a predeterminedorder, wherein the subset of the fourth list of data items includes afirst N data items in the fourth list of data items, and wherein N isless than or equal to the predetermined number (e.g., block 844).

In some examples, the spoken user request is associated with a user, andwherein the predetermined number is based on a degree of familiarity ofthe user with data items in the fourth list of data items.

In some examples, the predetermined number is based on an amount ofmetadata associated with each data item in the fourth list of dataitems.

In some examples, the predetermined number is based on a cognitive loadassociated with the user at the time the spoken user request isreceived.

In some examples, the fourth spoken response includes a second spokenprompt indicating that additional data items are available.

In some examples, the second spoken prompt indicates a number ofremaining data items in the fourth list of data items.

In some examples, processing unit 1008 is further configured to, whileproviding the fourth spoken response, detect (e.g., with detecting unit1022) a second user request (e.g., second user request in speech inputof block 814). Processing unit 1008 is further configured to, inresponse to detecting the second user request, cease (e.g., with ceasingunit 1024) to provide the fourth spoken response. Processing unit 1008is further configured to generate (e.g., with generating unit 1016) asixth spoken response based on the second user request and a portion ofthe fourth spoken response that coincides with detecting the second userrequest. Processing unit 1008 is further configured to provide (e.g.,with providing unit 1018 and speaker unit 1005) the sixth spokenresponse.

In some examples, the one or more attributes are determined based on afrequency that an attribute is specified in a plurality of user requestsfrom a plurality of users.

In some examples, the one or more attributes are determined based a userprofile associated with the spoken user request.

In some examples, determining the one or more attributes includesrandomly selecting an attribute from a category of attributes.

The operations described above with reference to FIG. 8A-D areoptionally implemented by components depicted in FIGS. 1-4, 6A-B, and7A-C. For example, the operations of process 800 may be implemented byone or more of operating system 718, applications module 724, I/Oprocessing module 728, STT processing module 730, natural languageprocessing module 732, dialogue flow processing module 734, task flowprocessing module 736, service processing module 738, or processor(s)220, 410, 704. It would be clear to a person having ordinary skill inthe art how other processes are implemented based on the componentsdepicted in FIGS. 1-4, 6A-B, and 7A-C.

In accordance with some implementations, a computer-readable storagemedium (e.g., a non-transitory computer readable storage medium) isprovided, the computer-readable storage medium storing one or moreprograms for execution by one or more processors of an electronicdevice, the one or more programs including instructions for performingany of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises means forperforming any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises a processing unitconfigured to perform any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises one or moreprocessors and memory storing one or more programs for execution by theone or more processors, the one or more programs including instructionsfor performing any of the methods or processes described herein.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the techniques and their practical applications. Othersskilled in the art are thereby enabled to best utilize the techniquesand various embodiments with various modifications as are suited to theparticular use contemplated.

Although the disclosure and examples have been fully described withreference to the accompanying drawings, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

As described above, one aspect of the present technology is thegathering and use of data available from various sources to improve thedelivery to users of invitational content or any other content that maybe of interest to them. The present disclosure contemplates that in someinstances, this gathered data may include personal information data thatuniquely identifies or can be used to contact or locate a specificperson. Such personal information data can include demographic data,location-based data, telephone numbers, email addresses, home addresses,or any other identifying information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used todeliver targeted content that is of greater interest to the user.Accordingly, use of such personal information data enables calculatedcontrol of the delivered content. Further, other uses for personalinformation data that benefit the user are also contemplated by thepresent disclosure.

The present disclosure further contemplates that the entitiesresponsible for the collection, analysis, disclosure, transfer, storage,or other use of such personal information data will comply withwell-established privacy policies and/or privacy practices. Inparticular, such entities should implement and consistently use privacypolicies and practices that are generally recognized as meeting orexceeding industry or governmental requirements for maintaining personalinformation data private and secure. For example, personal informationfrom users should be collected for legitimate and reasonable uses of theentity and not shared or sold outside of those legitimate uses. Further,such collection should occur only after receiving the informed consentof the users. Additionally, such entities would take any needed stepsfor safeguarding and securing access to such personal information dataand ensuring that others with access to the personal information dataadhere to their privacy policies and procedures. Further, such entitiescan subject themselves to evaluation by third parties to certify theiradherence to widely accepted privacy policies and practices.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof advertisement delivery services, the present technology can beconfigured to allow users to select to “opt in” or “opt out” ofparticipation in the collection of personal information data duringregistration for services. In another example, users can select not toprovide location information for targeted content delivery services. Inyet another example, users can select to not provide precise locationinformation, but permit the transfer of location zone information.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, content can beselected and delivered to users by inferring preferences based onnon-personal information data or a bare minimum amount of personalinformation, such as the content being requested by the deviceassociated with a user, other non-personal information available to thecontent delivery services, or publically available information.

What is claimed is:
 1. A non-transitory computer-readable storage mediumhaving instructions stored thereon, the instructions, when executed byone or more processors, cause the one or more processors to: receive aspoken user request associated with a plurality of data items; determinewhether a number of parameters defined in the spoken user request isless than a predetermined number of parameters; in response todetermining that a number of parameters defined in the spoken userrequest is less than a predetermined number of parameters: determine oneor more attributes related to the spoken user request, the one or moreattributes not defined in the spoken user request; obtain a list of dataitems based on the spoken user request and the one or more attributes;generate a spoken response comprising a subset of the list of dataitems; and provide the spoken response.
 2. The computer-readable storagemedium of claim 1, wherein the instructions further cause the one ormore processors to: determine a size of metadata associated with dataitems that satisfy the spoken user request, wherein the threshold levelis based on the size of the metadata.
 3. The computer-readable storagemedium of claim 1, wherein the instructions further cause the one ormore processors to: determine a domain corresponding to the spoken userrequest, wherein the threshold level is based on the domain.
 4. Thecomputer-readable storage medium of claim 1, wherein the instructionsfurther cause the one or more processors to: determine a degree offamiliarity associated with data items that satisfy the spoken userrequest, wherein the threshold level is based on the degree offamiliarity.
 5. The computer-readable storage medium of claim 1, whereingenerating a spoken response includes generating a spoken preamble thatdescribes an attribute of the one or more attributes, and whereinproviding the spoken response includes providing the spoken preambleprior to providing the subset of the list of data items.
 6. Thecomputer-readable storage medium of claim 1, wherein the spoken responseincludes a description that specifies an additional attribute for eachdata item of the subset of the list of data items, and wherein theadditional attribute is not defined in the spoken user request and isdifferent from any of the one or more attributes.
 7. Thecomputer-readable storage medium of claim 1, wherein the subset of thelist of data items has at most a predetermined number of data items. 8.The computer-readable storage medium of claim 1, wherein theinstructions further cause the one or more processors to: receive speechinput; in response to receiving the speech input, determine whether thespeech input corresponds to a rejection of the subset of the list ofdata items; and in response to determining that the speech inputcorresponds to a rejection of the subset of the list of data items:determine one or more second attributes related to the spoken userrequest, wherein the one or more second attributes are different fromthe one or more attributes and are not defined in the spoken userrequest; obtain a second list of data items based on the spoken userrequest and the one or more second attributes; generate a second spokenresponse comprising a subset of the second list of data items; andprovide the second spoken response.
 9. The computer-readable storagemedium of claim 8, wherein the instructions further cause the one ormore processors to: in response to determining that the speech inputcorresponds to a rejection of the subset of the list of data items,provide a spoken prompt for the user to provide additional attributes torefine the spoken user request.
 10. The computer-readable storage mediumof claim 9, wherein the instructions further cause the one or moreprocessors to: receive a second speech input responsive to the spokenprompt; obtain a third list of data items based on the spoken userrequest and one or more attributes defined in the second speech input;generate a third spoken response comprising a subset of the third listof data items; and provide the third spoken response.
 11. Thecomputer-readable storage medium of claim 8, wherein the instructionsfurther cause the one or more processors to: in response to determiningthat the speech input does not correspond to a rejection of the subsetof the list of data items, determine whether the speech inputcorresponds to an acceptance of a data item in the subset of the list ofdata items; and in response to determining that the speech inputcorresponds to an acceptance of a data item in the subset of the list ofdata items, provide content associated with the accepted data item. 12.The computer-readable storage medium of claim 1, wherein theinstructions further cause the one or more processors to: determinewhether each of a predetermined number of previous spoken responsesincludes a spoken prompt indicating that additional data items areavailable; in response to determining that each of a predeterminednumber of previous spoken responses does not include a spoken promptindicating that additional data items are available, provide, in thespoken response, a spoken prompt indicating that additional data itemsare available; and in response to determining that each of apredetermined number of previous spoken responses includes a spokenprompt indicating that additional data items are available, forgoproviding, in the spoken response, a spoken prompt indicating thatadditional data items are available.
 13. The computer-readable storagemedium of claim 1, wherein the instructions further cause the one ormore processors to: in response to determining that a number ofparameters defined in the spoken user request is not less than apredetermined number of parameters: obtain a fourth list of data itemsbased on the spoken user request; determine whether a number of dataitems in the fourth list of data items exceeds a predetermined number;and in response to determining that a number of data items in the fourthlist of data items exceeds a predetermined number: generate a fourthspoken response comprising a subset of the fourth list of data items;and provide the fourth spoken response.
 14. The computer-readablestorage medium of claim 13, wherein generating the fourth spokenresponse includes generating a fourth spoken preamble that indicates anumber of data items in the fourth list of data items.
 15. Thecomputer-readable storage medium of claim 13, wherein a number of dataitems in the subset of the fourth list of data items is less than orequal to the predetermined number.
 16. The computer-readable storagemedium of claim 13, wherein the instructions further cause the one ormore processors to: in response to determining that a number of dataitems in the fourth list of data items does not exceed a predeterminednumber: generate a fifth spoken response comprising the fourth list ofdata items; and provide the fifth spoken response.
 17. Thecomputer-readable storage medium of claim 13, wherein the instructionsfurther cause the one or more processors to: select, based on anattribute defined in the spoken user request, the subset of the fourthlist of data items from the fourth list of data items.
 18. An electronicdevice for operating a digital assistant, comprising: one or moreprocessors; and memory having instructions stored thereon, theinstructions, when executed by the one or more processors, cause the oneor more processors to: receive a spoken user request associated with aplurality of data items; determine whether a number of parametersdefined in the spoken user request is less than a predetermined numberof parameters; in response to determining that a number of parametersdefined in the spoken user request is less than a predetermined numberof parameters: determine one or more attributes related to the spokenuser request, the one or more attributes not defined in the spoken userrequest; obtain a list of data items based on the spoken user requestand the one or more attributes; generate a spoken response comprising asubset of the list of data items; and provide the spoken response.
 19. Amethod for operating a digital assistant, the method comprising: at anelectronic device with a processor and memory: receiving a spoken userrequest associated with a plurality of data items; whether a number ofparameters defined in the spoken user request is less than apredetermined number of parameters; in response to determining that anumber of parameters defined in the spoken user request is less than apredetermined number of parameters: determining one or more attributesrelated to the spoken user request, the one or more attributes notdefined in the spoken user request; obtaining a list of data items basedon the spoken user request and the one or more attributes; generating aspoken response comprising a subset of the list of data items; andproviding the spoken response.